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4.5 Outlier Analysis
A data set may contain objects that do not comply with the general behavior or model of the data. These data objects are outliers. Many data mining methods discard outliers as noise or exceptions. However, in some applications (e.g., fraud detection) the rare events can be more interesting than the more regularly occurring ones. The analysis of outlier data is referred to as outlier analysis or anomaly mining.
Outliers may be detected using statistical tests that assume a distribution or probability model for the data, or using distance measures where objects that are remote from any other cluster are considered outliers. Rather than using statistical or distance measures, density-based methods may identify outliers in a local region, although they look normal from a global statistical distribution view.
Example 10: Outlier analysis. Outlier analysis may uncover fraudulent usage of credit cards by detecting purchases of unusually large amounts for a given account number in comparison to regular charges incurred by the same account. Outlier values may also be detected with respect to the locations and types of purchase, or the purchase frequency.
Outlier analysis is discussed in Section 12.
4.6 Are All Patterns Interesting?
A data mining system has the potential to generate thousands or even millions of patterns, or rules.
You may ask, "Are all of the patterns interesting?" Typically, the answer is no-only a small fraction of the patterns potentially generated would actually be of interest to a given user.
This raises some serious questions for data mining. You may wonder, "What makes a pattern interesting? Can a data mining system generate all of the interesting patterns? Or, Can the system generate only the interesting ones?" To answer the first question, a pattern is interesting if it is (1) easily understood by humans, (2) valid on new or test data with some degree of certainty, (3) potentially useful, and (4) novel. A pattern is also interesting if it validates a hypothesis that the user sought to confirm. An interesting pattern represents knowledge.
Several objective measures of pattern interestingness exist. These are based on the structure of discovered patterns and the statistics underlying them. An objective measure for association rules of the form X )Y is rule support, representing the percentage of transactions from a transaction database that the given rule satisfies. This is taken to be the probability P.X [Y/, where X [Y indicates that a transaction contains both X and Y, that is, the union of item sets X and Y. Another objective measure for association rules is confidence, which assesses the degree of certainty of the detected association. This is taken to be the conditional probability P.YjX), that is, the probability that a transaction containing X also contains Y. More formally, support and confidence are defined as
support.X )Y/ D P.X [Y/,
confidence.X )Y/ D P.YjX/.
In general, each interestingness measure is associated with a threshold, which may be controlled by the user. For example, rules that do not satisfy a confidence threshold of, say, 50% can be considered uninteresting. Rules below the threshold likely reflect noise, exceptions, or minority cases and are probably of less value.
Other objective interestingness measures include accuracy and coverage for classification (IF-THEN) rules. In general terms, accuracy tells us the percentage of data that are correctly classified by a rule. Coverage is similar to support, in that it tells us the percentage of data to which a rule applies. Regarding understandability, we may use simple objective measures that assess the complexity or length in bits of the patterns mined.
Although objective measures help identify interesting patterns, they are often insufficient unless combined with subjective measures that reflect a particular user's needs and interests. For example, patterns describing the characteristics of customers who shop frequently at All Electronics should be interesting to the marketing manager, but may be of little interest to other analysts studying the same database for patterns on employee performance. Furthermore, many patterns that are interesting by objective standards may represent common sense and, therefore, are actually uninteresting.
Subjective interestingness measures are based on user beliefs in the data. These measures find patterns interesting if the patterns are unexpected (contradicting a user's belief) or offer strategic information on which the user can act. In the latter case, such patterns are referred to as actionable. For example, patterns like "a large earthquake often follows a cluster of small quakes" may be highly actionable if users can act on the information to save lives. Patterns that are expected can be interesting if they confirm a hypothesis that the user wishes to validate or they resemble a user's hunch.
The second question--"Can a data mining system generate all of the interesting patterns?"--refers to the completeness of a data mining algorithm. It is often unrealistic and inefficient for data mining systems to generate all possible patterns. Instead, user provided constraints and interestingness measures should be used to focus the search.
For some mining tasks, such as association, this is often sufficient to ensure the completeness of the algorithm. Association rule mining is an example where the use of constraints and interestingness measures can ensure the completeness of mining. The methods involved are examined in detail in Section 6.
Finally, the third question--"Can a data mining system generate only interesting pat terns?"--is an optimization problem in data mining. It is highly desirable for data mining systems to generate only interesting patterns. This would be efficient for users and data-mining systems because neither would have to search through the patterns generated to identify the truly interesting ones. Progress has been made in this direction; however, such optimization remains a challenging issue in data mining.
Measures of pattern interestingness are essential for the efficient discovery of patterns by target users. Such measures can be used after the data mining step to rank the discovered patterns according to their interestingness, filtering out the uninteresting ones.
More important, such measures can be used to guide and constrain the discovery pro cess, improving the search efficiency by pruning away subsets of the pattern space that do not satisfy prespecified interestingness constraints. Examples of such a constraint based mining process are described in Section 7 (with respect to pattern discovery) and Section 11 (with respect to clustering).
Methods to assess pattern interestingness, and their use to improve data mining efficiency, are discussed throughout the guide with respect to each kind of pattern that can be mined.
5. Which Technologies Are Used?
As a highly application-driven domain, data mining has incorporated many techniques from other domains such as statistics, machine learning, pattern recognition, database and data warehouse systems, information retrieval, visualization, algorithms, high performance computing, and many application domains (FIG. 11). The interdisciplinary nature of data mining research and development contributes significantly to the success of data mining and its extensive applications. In this section, we give examples of several disciplines that strongly influence the development of data mining methods.
Statistics studies the collection, analysis, interpretation or explanation, and presentation of data. Data mining has an inherent connection with statistics.
A statistical model is a set of mathematical functions that describe the behavior of the objects in a target class in terms of random variables and their associated probability distributions. Statistical models are widely used to model data and data classes.
For example, in data mining tasks like data characterization and classification, statistical models of target classes can be built. In other words, such statistical models can be the outcome of a data mining task. Alternatively, data mining tasks can be built on top of statistical models. For example, we can use statistics to model noise and missing data values. Then, when mining patterns in a large data set, the data mining process can use the model to help identify and handle noisy or missing values in the data.
Statistics research develops tools for prediction and forecasting using data and statistical models. Statistical methods can be used to summarize or describe a collection of data. Basic statistical descriptions of data are introduced in Section 2. Statistics is useful for mining various patterns from data as well as for understanding the underlying mechanisms generating and affecting the patterns. Inferential statistics (or predictive statistics) models data in a way that accounts for randomness and uncertainty in the observations and is used to draw inferences about the process or population under investigation.
Statistical methods can also be used to verify data mining results. For example, after a classification or prediction model is mined, the model should be verified by statistical hypothesis testing. A statistical hypothesis test (sometimes called confirmatory data analysis)makes statistical decisions using experimental data. A result is called statistically significant if it is unlikely to have occurred by chance. If the classification or prediction model holds true, then the descriptive statistics of the model increases the soundness of the model.
Applying statistical methods in data mining is far from trivial. Often, a serious challenge is how to scale up a statistical method over a large data set. Many statistical methods have high complexity in computation. When such methods are applied on large data sets that are also distributed on multiple logical or physical sites, algorithms should be carefully designed and tuned to reduce the computational cost. This challenge becomes even tougher for online applications, such as online query suggestions in search engines, where data mining is required to continuously handle fast, real-time data streams.
5.2 Machine Learning
Machine learning investigates how computers can learn (or improve their performance) based on data. A main research area is for computer programs to automatically learn to recognize complex patterns and make intelligent decisions based on data. For example, a typical machine learning problem is to program a computer so that it can automatically recognize handwritten postal codes on mail after learning from a set of examples.
Machine learning is a fast-growing discipline. Here, we illustrate classic problems in machine learning that are highly related to data mining.
Supervised learning is basically a synonym for classification. The supervision in the learning comes from the labeled examples in the training data set. For example, in the postal code recognition problem, a set of handwritten postal code images and their corresponding machine-readable translations are used as the training examples, which supervise the learning of the classification model.
Unsupervised learning is essentially a synonym for clustering. The learning process is unsupervised since the input examples are not class labeled. Typically, we may use clustering to discover classes within the data. For example, an unsupervised learning method can take, as input, a set of images of handwritten digits. Suppose that it finds 10 clusters of data. These clusters may correspond to the 10 distinct digits of 0 to 9, respectively. However, since the training data are not labeled, the learned model cannot tell us the semantic meaning of the clusters found.
Semi-supervised learning is a class of machine learning techniques that make use of both labeled and unlabeled examples when learning a model. In one approach, labeled examples are used to learn class models and unlabeled examples are used to refine the boundaries between classes. For a two-class problem, we can think of the set of examples belonging to one class as the positive examples and those belonging to the other class as the negative examples. In FIG. 12, if we do not consider the unlabeled examples, the dashed line is the decision boundary that best partitions the positive examples from the negative examples. Using the unlabeled examples, we can refine the decision boundary to the solid line. Moreover, we can detect that the two positive examples at the top right corner, though labeled, are likely noise or outliers.
Active learning is a machine learning approach that lets users play an active role in the learning process. An active learning approach can ask a user (e.g., a domain expert) to label an example, which may be from a set of unlabeled examples or synthesized by the learning program. The goal is to optimize the model quality by actively acquiring knowledge from human users, given a constraint on how many examples they can be asked to label.
You can see there are many similarities between data mining and machine learning.
For classification and clustering tasks, machine learning research often focuses on the accuracy of the model. In addition to accuracy, data mining research places strong emphasis on the efficiency and scalability of mining methods on large data sets, as well as on ways to handle complex types of data and explore new, alternative methods.
5.3 Database Systems and Data Warehouses
Database systems research focuses on the creation, maintenance, and use of databases for organizations and end-users. Particularly, database systems researchers have established highly recognized principles in data models, query languages, query processing and optimization methods, data storage, and indexing and accessing methods. Database systems are often well known for their high scalability in processing very large, relatively structured data sets.
Many data mining tasks need to handle large data sets or even real-time, fast streaming data. Therefore, data mining can make good use of scalable database technologies to achieve high efficiency and scalability on large data sets. Moreover, data-mining tasks can be used to extend the capability of existing database systems to satisfy advanced users' sophisticated data analysis requirements.
Recent database systems have built systematic data analysis capabilities on database data using data warehousing and data mining facilities. A data warehouse integrates data originating from multiple sources and various timeframes. It consolidates data in multidimensional space to form partially materialized data cubes. The data cube model not only facilitates OLAP in multidimensional databases but also promotes multidimensional data mining.
5.4 Information Retrieval
Information retrieval (IR) is the science of searching for documents or information in documents. Documents can be text or multimedia, and may reside on the Web. The differences between traditional information retrieval and database systems are twofold:
Information retrieval assumes that (1) the data under search are unstructured; and (2) the queries are formed mainly by keywords, which do not have complex structures (unlike SQL queries in database systems).
The typical approaches in information retrieval adopt probabilistic models. For example, a text document can be regarded as a bag of words, that is, a multiset of words appearing in the document. The document's language model is the probability density function that generates the bag of words in the document. The similarity between two documents can be measured by the similarity between their corresponding language models.
Furthermore, a topic in a set of text documents can be modeled as a probability distribution over the vocabulary, which is called a topic model. A text document, which may involve one or multiple topics, can be regarded as a mixture of multiple topic models. By integrating information retrieval models and data mining techniques, we can find the major topics in a collection of documents and, for each document in the collection, the major topics involved.
Increasingly large amounts of text and multimedia data have been accumulated and made available online due to the fast growth of the Web and applications such as dig ital libraries, digital governments, and health care information systems. Their effective search and analysis have raised many challenging issues in data mining. Therefore, text mining and multimedia data mining, integrated with information retrieval methods, have become increasingly important.
6. Which Kinds of Applications Are Targeted?
Where there are data, there are data mining applications As a highly application-driven discipline, data mining has seen great successes in many applications. It is impossible to enumerate all applications where data mining plays a critical role. Presentations of data mining in knowledge-intensive application domains, such as bioinformatics and software engineering, require more in-depth treatment and are beyond the scope of this guide. To demonstrate the importance of applications as a major dimension in data mining research and development, we briefly discuss two highly successful and popular application examples of data mining: business intelligence and search engines.
6.1 Business Intelligence
It is critical for businesses to acquire a better understanding of the commercial context of their organization, such as their customers, the market, supply and resources, and competitors. Business intelligence (BI) technologies provide historical, current, and predictive views of business operations. Examples include reporting, online analytical processing, business performance management, competitive intelligence, benchmarking, and predictive analytics.
"How important is business intelligence?" Without data mining, many businesses may not be able to perform effective market analysis, compare customer feedback on similar products, discover the strengths and weaknesses of their competitors, retain highly valuable customers, and make smart business decisions.
Clearly, data mining is the core of business intelligence. Online analytical processing tools in business intelligence rely on data warehousing and multidimensional data mining. Classification and prediction techniques are the core of predictive analytics in business intelligence, for which there are many applications in analyzing markets, supplies, and sales. Moreover, clustering plays a central role in customer relationship management, which groups customers based on their similarities. Using characterization mining techniques, we can better understand features of each customer group and develop customized customer reward programs.
6.2 Web Search Engines
A Web search engine is a specialized computer server that searches for information on the Web. The search results of a user query are often returned as a list (sometimes called hits). The hits may consist of web pages, images, and other types of files. Some search engines also search and return data available in public databases or open directories. Search engines differ from web directories in that web directories are maintained by human editors whereas search engines operate algorithmically or by a mixture of algorithmic and human input.
Web search engines are essentially very large data mining applications. Various data mining techniques are used in all aspects of search engines, ranging from crawling (e.g., deciding which pages should be crawled and the crawling frequencies), indexing (e.g., selecting pages to be indexed and deciding to which extent the index should be constructed), and searching (e.g., deciding how pages should be ranked, which advertisements should be added, and how the search results can be personalized or made "context aware").
Search engines pose grand challenges to data mining. First, they have to handle a huge and ever-growing amount of data. Typically, such data cannot be processed using one or a few machines. Instead, search engines often need to use computer clouds, which consist of thousands or even hundreds of thousands of computers that collaboratively mine the huge amount of data. Scaling up data mining methods over computer clouds and large distributed data sets is an area for further research.
Second, Web search engines often have to deal with online data. A search engine may be able to afford constructing a model offline on huge data sets. To do this, it may construct a query classifier that assigns a search query to predefined categories based on the query topic (i.e., whether the search query "apple" is meant to retrieve information about a fruit or a brand of computers). Whether a model is constructed offline, the application of the model online must be fast enough to answer user queries in real time.
Another challenge is maintaining and incrementally updating a model on fast growing data streams. For example, a query classifier may need to be incrementally maintained continuously since new queries keep emerging and predefined categories and the data distribution may change. Most of the existing model training methods are offline and static and thus cannot be used in such a scenario.
Third, Web search engines often have to deal with queries that are asked only a very small number of times. Suppose a search engine wants to provide context-aware query recommendations. That is, when a user poses a query, the search engine tries to infer the context of the query using the user's profile and his query history in order to return more customized answers within a small fraction of a second. However, although the total number of queries asked can be huge, most of the queries may be asked only once or a few times. Such severely skewed data are challenging for many data mining and machine learning methods.
[ A Web crawler is a computer program that browses the Web in a methodical, automated manner.]
7. Major Issues in Data Mining
Life is short but art is long. - Hippocrates
Data mining is a dynamic and fast-expanding field with great strengths. In this section, we briefly outline the major issues in data mining research, partitioning them into five groups: mining methodology, user interaction, efficiency and scalability, diversity of data types, and data mining and society. Many of these issues have been addressed in recent data mining research and development to a certain extent and are now considered data mining requirements; others are still at the research stage. The issues continue to stimulate further investigation and improvement in data mining.
7.1 Mining Methodology
Researchers have been vigorously developing new data mining methodologies. This involves the investigation of new kinds of knowledge, mining in multidimensional space, integrating methods from other disciplines, and the consideration of semantic ties among data objects. In addition, mining methodologies should consider issues such as data uncertainty, noise, and incompleteness. Some mining methods explore how user specified measures can be used to assess the interestingness of discovered patterns as well as guide the discovery process. Let's have a look at these various aspects of mining methodology.
Mining various and new kinds of knowledge: Data mining covers a wide spectrum of data analysis and knowledge discovery tasks, from data characterization and discrimination to association and correlation analysis, classification, regression, clustering, outlier analysis, sequence analysis, and trend and evolution analysis. These tasks may use the same database in different ways and require the development of numerous data mining techniques. Due to the diversity of applications, new mining tasks continue to emerge, making data mining a dynamic and fast-growing field. For example, for effective knowledge discovery in information networks, integrated clustering and ranking may lead to the discovery of high-quality clusters and object ranks in large networks.
Mining knowledge in multidimensional space: When searching for knowledge in large data sets, we can explore the data in multidimensional space. That is, we can search for interesting patterns among combinations of dimensions (attributes) at varying levels of abstraction. Such mining is known as (exploratory) multidimensional data mining. In many cases, data can be aggregated or viewed as a multidimensional data cube. Mining knowledge in cube space can substantially enhance the power and flexibility of data mining.
Data mining-an interdisciplinary effort: The power of data mining can be substantially enhanced by integrating new methods from multiple disciplines. For example, to mine data with natural language text, it makes sense to fuse data mining methods with methods of information retrieval and natural language processing. As another example, consider the mining of software bugs in large programs. This form of mining, known as bug mining, benefits from the incorporation of software engineering knowledge into the data mining process.
Boosting the power of discovery in a networked environment: Most data objects reside in a linked or interconnected environment, whether it be the Web, database relations, files, or documents. Semantic links across multiple data objects can be used to advantage in data mining. Knowledge derived in one set of objects can be used to boost the discovery of knowledge in a "related" or semantically linked set of objects.
Handling uncertainty, noise, or incompleteness of data: Data often contain noise, errors, exceptions, or uncertainty, or are incomplete. Errors and noise may confuse the data mining process, leading to the derivation of erroneous patterns. Data cleaning, data preprocessing, outlier detection and removal, and uncertainty reasoning are examples of techniques that need to be integrated with the data mining process.
Pattern evaluation and pattern- or constraint-guided mining: Not all the patterns generated by data mining processes are interesting. What makes a pattern interesting may vary from user to user. Therefore, techniques are needed to assess the interestingness of discovered patterns based on subjective measures. These estimate the value of patterns with respect to a given user class, based on user beliefs or expectations. Moreover, by using interestingness measures or user-specified constraints to guide the discovery process, we may generate more interesting patterns and reduce the search space.
7.2 User Interaction
The user plays an important role in the data mining process. Interesting areas of research include how to interact with a data mining system, how to incorporate a user's back ground knowledge in mining, and how to visualize and comprehend data mining results.
We introduce each of these here.
Interactive mining: The data mining process should be highly interactive. Thus, it is important to build flexible user interfaces and an exploratory mining environment, facilitating the user's interaction with the system. A user may like to first sample a set of data, explore general characteristics of the data, and estimate potential mining results. Interactive mining should allow users to dynamically change the focus of a search, to refine mining requests based on returned results, and to drill, dice, and pivot through the data and knowledge space interactively, dynamically exploring "cube space" while mining.
Incorporation of background knowledge: Background knowledge, constraints, rules, and other information regarding the domain under study should be incorporated into the knowledge discovery process. Such knowledge can be used for pattern evaluation as well as to guide the search toward interesting patterns.
Ad hoc data mining and data mining query languages: Query languages (e.g., SQL) have played an important role in flexible searching because they allow users to pose ad hoc queries. Similarly, high-level data mining query languages or other high-level flexible user interfaces will give users the freedom to define ad hoc data mining tasks.
This should facilitate specification of the relevant sets of data for analysis, the domain knowledge, the kinds of knowledge to be mined, and the conditions and constraints to be enforced on the discovered patterns. Optimization of the processing of such flexible mining requests is another promising area of study.
Presentation and visualization of data mining results: How can a data mining system present data mining results, vividly and flexibly, so that the discovered knowledge can be easily understood and directly usable by humans? This is especially crucial if the data mining process is interactive. It requires the system to adopt expressive knowledge representations, user-friendly interfaces, and visualization techniques.
7.3 Efficiency and Scalability
Efficiency and scalability are always considered when comparing data mining algorithms. As data amounts continue to multiply, these two factors are especially critical.
Efficiency and scalability of data mining algorithms: Data mining algorithms must be efficient and scalable in order to effectively extract information from huge amounts of data in many data repositories or in dynamic data streams. In other words, the running time of a data mining algorithm must be predictable, short, and acceptable by applications. Efficiency, scalability, performance, optimization, and the ability to execute in real time are key criteria that drive the development of many new data mining algorithms.
Parallel, distributed, and incremental mining algorithms: The humongous size of many data sets, the wide distribution of data, and the computational complexity of some data mining methods are factors that motivate the development of parallel and distributed data-intensive mining algorithms. Such algorithms first partition the data into "pieces." Each piece is processed, in parallel, by searching for patterns. The parallel processes may interact with one another. The patterns from each partition are eventually merged.
Cloud computing and cluster computing, which use computers in a distributed and collaborative way to tackle very large-scale computational tasks, are also active research themes in parallel data mining. In addition, the high cost of some data mining processes and the incremental nature of input promote incremental data mining, which incorporates new data updates without having to mine the entire data "from scratch." Such methods perform knowledge modification incrementally to amend and strengthen what was previously discovered.
7.4 Diversity of Database Types
The wide diversity of database types brings about challenges to data mining. These include
Handling complex types of data: Diverse applications generate a wide spectrum of new data types, from structured data such as relational and data warehouse data to semi-structured and unstructured data; from stable data repositories to dynamic data streams; from simple data objects to temporal data, biological sequences, sensor data, spatial data, hypertext data, multimedia data, software program code, Web data, and social network data. It is unrealistic to expect one data mining system to mine all kinds of data, given the diversity of data types and the different goals of data mining.
Domain- or application-dedicated data mining systems are being constructed for in depth mining of specific kinds of data. The construction of effective and efficient data mining tools for diverse applications remains a challenging and active area of research.
Mining dynamic, networked, and global data repositories: Multiple sources of data are connected by the Internet and various kinds of networks, forming gigantic, distributed, and heterogeneous global information systems and networks. The discovery of knowledge from different sources of structured, semi-structured, or unstructured yet interconnected data with diverse data semantics poses great challenges to data mining. Mining such gigantic, interconnected information networks may help disclose many more patterns and knowledge in heterogeneous data sets than can be discovered from a small set of isolated data repositories. Web mining, multisource data mining, and information network mining have become challenging and fast-evolving data mining fields.
7.5 Data Mining and Society
How does data mining impact society? What steps can data mining take to preserve the privacy of individuals? Do we use data mining in our daily lives without even knowing that we do? These questions raise the following issues:
Social impacts of data mining: With data mining penetrating our everyday lives, it is important to study the impact of data mining on society. How can we use data mining technology to benefit society? How can we guard against its misuse? The improper disclosure or use of data and the potential violation of individual privacy and data protection rights are areas of concern that need to be addressed.
Privacy-preserving data mining: Data mining will help scientific discovery, business management, economy recovery, and security protection (e.g., the real-time discovery of intruders and cyber attacks). However, it poses the risk of disclosing an individual's personal information. Studies on privacy-preserving data publishing and data mining are ongoing. The philosophy is to observe data sensitivity and preserve people's privacy while performing successful data mining.
Invisible data mining: We cannot expect everyone in society to learn and master data mining techniques. More and more systems should have data mining functions built within so that people can perform data mining or use data mining results simply by mouse clicking, without any knowledge of data mining algorithms. Intelligent search engines and Internet-based stores perform such invisible data mining by incorporating data mining into their components to improve their functionality and performance. This is done often unbeknownst to the user. For example, when purchasing items online, users may be unaware that the store is likely collecting data on the buying patterns of its customers, which may be used to recommend other items for purchase in the future.
These issues and many additional ones relating to the research, development, and application of data mining are discussed throughout the guide.
Necessity is the mother of invention. With the mounting growth of data in every application, data mining meets the imminent need for effective, scalable, and flexible data analysis in our society. Data mining can be considered as a natural evolution of information technology and a confluence of several related disciplines and application domains.
Data mining is the process of discovering interesting patterns from massive amounts of data. As a knowledge discovery process, it typically involves data cleaning, data integration, data selection, data transformation, pattern discovery, pattern evaluation, and knowledge presentation.
A pattern is interesting if it is valid on test data with some degree of certainty, novel, potentially useful (e.g., can be acted on or validates a hunch about which the user was curious), and easily understood by humans. Interesting patterns represent knowledge. Measures of pattern interestingness, either objective or subjective, can be used to guide the discovery process.
We present a multidimensional view of data mining. The major dimensions are data, knowledge, technologies, and applications.
Data mining can be conducted on any kind of data as long as the data are meaningful for a target application, such as database data, data warehouse data, transactional data, and advanced data types. Advanced data types include time-related or sequence data, data streams, spatial and spatiotemporal data, text and multimedia data, graph and networked data, and Web data.
A data warehouse is a repository for long-term storage of data from multiple sources, organized so as to facilitate management decision making. The data are stored under a unified schema and are typically summarized. Data warehouse systems pro vide multidimensional data analysis capabilities, collectively referred to as online analytical processing.
Multidimensional data mining (also called exploratory multidimensional data mining) integrates core data mining techniques with OLAP-based multidimensional analysis. It searches for interesting patterns among multiple combinations of dimensions (attributes) at varying levels of abstraction, thereby exploring multi dimensional data space.
Data mining functionalities are used to specify the kinds of patterns or knowledge to be found in data mining tasks. The functionalities include characterization and discrimination; the mining of frequent patterns, associations, and correlations; classification and regression; cluster analysis; and outlier detection. As new types of data, new applications, and new analysis demands continue to emerge, there is no doubt we will see more and more novel data mining tasks in the future.
Data mining, as a highly application-driven domain, has incorporated technologies from many other domains. These include statistics, machine learning, database and data warehouse systems, and information retrieval. The interdisciplinary nature of data mining research and development contributes significantly to the success of data mining and its extensive applications.
Data mining has many successful applications, such as business intelligence, Web search, bioinformatics, health informatics, finance, digital libraries, and digital governments.
There are many challenging issues in data mining research. Areas include mining methodology, user interaction, efficiency and scalability, and dealing with diverse data types. Data mining research has strongly impacted society and will continue to do so in the future.
1 What is data mining? In your answer, address the following:
(a) Is it another hype?
(b) Is it a simple transformation or application of technology developed from databases, statistics, machine learning, and pattern recognition?
(c) We have presented a view that data mining is the result of the evolution of database technology. Do you think that data mining is also the result of the evolution of machine learning research? Can you present such views based on the historical progress of this discipline? Address the same for the fields of statistics and pattern recognition.
(d) Describe the steps involved in data mining when viewed as a process of knowledge discovery.
2 How is a data warehouse different from a database? How are they similar?
3 Define each of the following data mining functionalities: characterization, discrimination, association and correlation analysis, classification, regression, clustering, and outlier analysis. Give examples of each data mining functionality, using a real-life database that you are familiar with.
4 Present an example where data mining is crucial to the success of a business. What data mining functionalities does this business need (e.g., think of the kinds of patterns that could be mined)? Can such patterns be generated alternatively by data query processing or simple statistical analysis?
5 Explain the difference and similarity between discrimination and classification, between characterization and clustering, and between classification and regression.
6 Based on your observations, describe another possible kind of knowledge that needs to be discovered by data mining methods but has not been listed in this section. Does it require a mining methodology that is quite different from those outlined in this section?
7 Outliers are often discarded as noise. However, one person's garbage could be another's treasure. For example, exceptions in credit card transactions can help us detect the fraudulent use of credit cards. Using fraudulence detection as an example, propose two methods that can be used to detect outliers and discuss which one is more reliable.
8 Describe three challenges to data mining regarding data mining methodology and user interaction issues.
1.9 What are the major challenges of mining a huge amount of data (e.g., billions of tuples) in comparison with mining a small amount of data (e.g., data set of a few hundred tuple)?
10 Outline the major research challenges of data mining in one specific application domain, such as stream/sensor data analysis, spatiotemporal data analysis, or bioinformatics.
The book Knowledge Discovery in Databases, edited by Piatetsky-Shapiro and Frawley [P-SF91], is an early collection of research papers on knowledge discovery from data.
The book Advances in Knowledge Discovery and Data Mining, edited by Fayyad, Piatetsky-Shapiro, Smyth, and Uthurusamy [FPSS+96], is a collection of later research results on knowledge discovery and data mining. There have been many data mining books published in recent years, including The Elements of Statistical Learning by Hastie, Tibshirani, and Friedman [HTF09]; Introduction to Data Mining by Tan, Steinbach, and Kumar [TSK05]; Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations by Witten, Frank, and Hall [WFH11]; Predictive Data Mining by Weiss and Indurkhya [WI98]; Mastering Data Mining: The Art and Science of Customer Relationship Management by Berry and Linoff [BL99]; Principles of Data Mining (Adaptive Computation and Machine Learning) by Hand,Mannila, and Smyth [HMS01]; Mining the Web: Discovering Knowledge from Hypertext Data by Chakrabarti [Cha03a]; Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data by Liu [Liu06]; Data Mining: Introductory and Advanced Topics by Dunham
[Dun03]; and Data Mining: Multimedia, Soft Computing, and Bioinformatics by Mitra and Acharya [MA03].
There are also books that contain collections of papers or sections on particular aspects of knowledge discovery-for example, Relational Data Mining edited by Dzeroski and Lavrac [De01]; Mining Graph Data edited by Cook and Holder [CH07]; Data Streams: Models and Algorithms edited by Aggarwal [Agg06]; Next Generation of Data Mining edited by Kargupta, Han, Yu, et al. [KHYC08]; Multimedia Data Mining: A Systematic Introduction to Concepts and Theory edited by Z. Zhang and R. Zhang [ZZ09];
Geographic Data Mining and Knowledge Discovery edited by Miller and Han [MH09];
and Link Mining: Models, Algorithms and Applications edited by Yu, Han, and Faloutsos [YHF10]. There are many tutorial notes on data mining in major databases, data mining, machine learning, statistics, and Web technology conferences.
KDNuggets is a regular electronic newsletter containing information relevant to knowledge discovery and data mining, moderated by Piatetsky-Shapiro since 1991.
The Internet site KDNuggets (www.kdnuggets.com) contains a good collection of KDD related information.
The data mining community started its first international conference on knowledge discovery and data mining in 1995. The conference evolved from the four inter national workshops on knowledge discovery in databases, held from 1989 to 1994.
ACM-SIGKDD, a Special Interest Group on Knowledge Discovery in Databases was set up under ACM in 1998 and has been organizing the international conferences on knowledge discovery and data mining since 1999. IEEE Computer Science Society has organized its annual data mining conference, International Conference on Data Mining (ICDM), since 2001. SIAM (Society on Industrial and Applied Mathematics) has organized its annual data mining conference, SIAM Data Mining Conference (SDM), since 2002. A dedicated journal, Data Mining and Knowledge Discovery, published by Kluwers Publishers, has been available since 1997. An ACM journal, ACM Transactions on Knowledge Discovery from Data, published its first volume in 2007.
ACM-SIGKDD also publishes a bi-annual newsletter, SIGKDD Explorations. There are a few other international or regional conferences on data mining, such as the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), and the International Conference on Data Warehousing and Knowledge Discovery (DaWaK).
Research in data mining has also been published in books, conferences, and journals on databases, statistics, machine learning, and data visualization. References to such sources are listed at the end of the book.
Popular textbooks on database systems include: Database Systems: The Complete Book by Garcia-Molina, Ullman, and Widom [GMUW08]; Database Management Systems by Ramakrishnan and Gehrke [RG03]; Database System Concepts by Silberschatz, Korth, and Sudarshan [SKS10]; and Fundamentals of Database Systems by Elmasri and Navathe [EN10]. For an edited collection of seminal articles on database systems, see Readings in Database Systems by Hellerstein and Stonebraker [HS05].
There are also many books on data warehouse technology, systems, and applications, such as: The DataWarehouse Toolkit: The Complete Guide to Dimensional Modeling by Kimball and Ross [KR02]; The Data Warehouse Lifecycle Toolkit by Kimball, Ross, Thornthwaite, and Mundy [KRTM08]; Mastering Data Warehouse Design: Relational and Dimensional Techniques by Imhoff, Galemmo, and Geiger [IGG03]; and Building the Data Warehouse by Inmon [Inm96]. A set of research papers on materialized views and data warehouse implementations were collected in Materialized Views: Techniques, Implementations, and Applications by Gupta and Mumick [GM99]. Chaudhuri and Dayal [CD97] present an early comprehensive overview of data warehouse technology.
Research results relating to data mining and data warehousing have been published in the proceedings of many international database conferences, including the ACM-SIGMOD International Conference on Management of Data (SIGMOD), the International Conference on Very Large Data Bases (VLDB), the ACM SIGACT SIGMOD-SIGART Symposium on Principles of Database Systems (PODS), the Inter national Conference on Data Engineering (ICDE), the International Conference on Extending Database Technology (EDBT), the International Conference on Database Theory (ICDT), the International Conference on Information and Knowledge Management (CIKM), the International Conference on Database and Expert Systems Applications (DEXA), and the International Symposium on Database Systems for Advanced Applications (DASFAA). Research in data mining is also published in major database journals, such as IEEE Transactions on Knowledge and Data Engineering (TKDE), ACM Transactions on Database Systems (TODS), Information Systems, The VLDB Journal, Data and Knowledge Engineering, International Journal of Intelligent Information Systems (JIIS), and Knowledge and Information Systems (KAIS).
Many effective data mining methods have been developed by statisticians and introduced in a rich set of textbooks. An overview of classification from a statistical pattern recognition perspective can be found in Pattern Classification by Duda, Hart, and Stork [DHS01]. There are also many textbooks covering regression and other topics in statistical analysis, such as Mathematical Statistics: Basic Ideas and Selected Topics by Bickel and Doksum [BD01]; The Statistical Sleuth: A Course in Methods of Data Analysis by Ramsey and Schafer [RS01]; Applied Linear Statistical Models by Neter, Kutner, Nachtsheim, and Wasserman [NKNW96]; An Introduction to Generalized Linear Models by Dobson [Dob90]; Applied Statistical Time Series Analysis by Shumway [Shu88]; and Applied Multivariate Statistical Analysis by Johnson and Wichern [JW92].
Research in statistics is published in the proceedings of several major statistical conferences, including Joint Statistical Meetings, International Conference of the Royal Statistical Society and Symposium on the Interface: Computing Science and Statistics.
Other sources of publication include the Journal of the Royal Statistical Society, The Annals of Statistics, the Journal of American Statistical Association, Technometrics, and Biometrika.
Textbooks and reference books on machine learning and pattern recognition include: Machine Learning by Mitchell [Mit97]; Pattern Recognition and Machine Learning by Bishop [Bis06]; Pattern Recognition by Theodoridis and Koutroumbas [TK08]; Introduction to Machine Learning by Alpaydin [Alp11]; Probabilistic Graphical Models: Principles and Techniques by Koller and Friedman [KF09]; and Machine Learning: An Algorithmic Perspective byMarsland [Mar09]. For an edited collection of seminal articles on machine learning, see Machine Learning, An Artificial Intelligence Approach, Volumes 1 through 4, edited by Michalski et al. [MCM83, MCM86, KM90, MT94], and Readings in Machine Learning by Shavlik and Dietterich [SD90].
Machine learning and pattern recognition research is published in the proceedings of several major machine learning, artificial intelligence, and pattern recognition conferences, including the International Conference on Machine Learning (ML), the ACM Conference on Computational Learning Theory (COLT), the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), the International Conference on Pattern Recognition (ICPR), the International Joint Conference on Artificial Intelligence (IJCAI), and the American Association of Artificial Intelligence Conference (AAAI). Other sources of publication include major machine learning, artificial intelligence, pattern recognition, and knowledge system journals, some of which have been mentioned before. Others include Machine Learning (ML), Pattern Recognition (PR), Artificial Intelligence Journal (AI), IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), and Cognitive Science.
Textbooks and reference books on information retrieval include Introduction to Information Retrieval by Manning, Raghavan, and Schutz [MRS08]; Information Retrieval: Implementing and Evaluating Search Engines by Buttcher, Clarke, and Cormack [BCC10]; Search Engines: Information Retrieval in Practice by Croft, Metzler, and Strohman [CMS09];Modern Information Retrieval: The Concepts and Technology Behind Search by Baeza-Yates and Ribeiro-Neto [BYRN11]; and Information Retrieval: Algorithms and Heuristics by Grossman and Frieder [GR04].
Information retrieval research is published in the proceedings of several information retrieval and Web search and mining conferences, including the International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), the International World Wide Web Conference (WWW), the ACM International Conference on Web Search and Data Mining (WSDM), the ACM Conference on Information and Knowledge Management (CIKM), the European Conference on Information Retrieval (ECIR), the Text Retrieval Conference (TREC), and the ACM/IEEE Joint Conference on Digital Libraries (JCDL). Other sources of publication include major information retrieval, information systems, and Web journals, such as Journal of Information Retrieval, ACM Transactions on Information Systems (TOIS), Information Processing and Management, Knowledge and Information Systems (KAIS), and IEEE Transactions on Knowledge and Data Engineering (TKDE).
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