The book is organized as follows.
Chapter 1 provides an introduction to the multidisciplinary field of data mining. It discusses the evolutionary path of database technology which led up to the need for data mining, and the importance of its application potential. The basic architecture of data mining systems is described, and a brief introduction to the concepts of database systems and data warehouses is given. A detailed classification of data mining tasks is presented, based on the different kinds of knowledge to be mined. A classification of data mining systems is presented, and major challenges in the field are discussed.
Chapter 2 is an introduction to data warehouses and OLAP (On-Line Analytical Processing). Topics include the concept of data warehouses and multidimensional databases, the construction of data cubes, the implementation of on-line analytical processing, and the relationship between data warehousing and data mining.
Chapter 3 describes techniques for preprocessing the data prior to mining. Methods of data cleaning, data integration and transformation, and data reduction are discussed, including the use of concept hierarchies for dynamic and static discretization. The automatic generation of concept hierarchies is also described.
Chapter 4 introduces the primitives of data mining which define the specification of a data mining task. It describes a data mining query language (DMQL), and provides examples of data mining queries. Other topics include the construction of graphical user interfaces, and the specification and manipulation of concept hierarchies.
Chapter 5 describes techniques for concept description, including characterization and discrimination. An attribute-oriented generalization technique is introduced, as well as its different implementations including a generalized relation technique and a multidimensional data cube technique. Several forms of knowledge presentation and visualization are illustrated. Relevance analysis is discussed. Methods for class comparison at multiple abstraction levels, and methods for the extraction of characteristic rules and discriminant rules with interestingness measurements are presented. In addition, statistical measures for descriptive mining are discussed.
Chapter 6 presents methods for mining association rules in transaction databases as well as relational databases and data warehouses. It includes a classification of association rules, a presentation of the basic Apriori algorithm and its variations, and techniques for mining multiple-level association rules, multidimensional association rules, quantitative association rules, and correlation rules. Strategies for finding interesting rules by constraint-based mining and the use of interestingness measures to focus the rule search are also described.
Chapter 7 describes methods for data classification and predictive modeling. Major methods of classification and prediction are explained, including decision tree induction, Bayesian classification, the neural network technique of backpropagation, k-nearest neighbor classifiers, case-based reasoning, genetic algorithms, rough set theory, and fuzzy set approaches. Association-based classification, which applies association rule mining to the problem of classification, is presented. Methods of regression are introduced, and issues regarding classifier accuracy are discussed.
Chapter 8 describes methods of clustering analysis. It first introduces the concept of data clustering and then presents several major data clustering approaches, including partition-based clustering, hierarchical clustering, and model-based clustering. Methods for clustering continuous data, discrete data, and data in multidimensional data cubes are presented. The scalability of clustering algorithms is discussed in detail.
Chapter 9 discusses methods for data mining in advanced database systems. It includes data mining in object-oriented databases, spatial databases, text databases, multimedia databases, active databases, temporal databases, heterogeneous and legacy databases, and resource and knowledge discovery in the Internet information base.
Finally, in Chapter 10, we summarize the concepts presented in this book and discuss applications of data mining and some challenging research issues.
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