Data Mining_ Concepts and Techniques - Jiawei Han [6]
Chapter 8 and Chapter 9 describe methods for data classification. Due to the importance and diversity of classification methods, the contents are partitioned into two chapters. Chapter 8 introduces basic concepts and methods for classification, including decision tree induction, Bayes classification, and rule-based classification. It also discusses model evaluation and selection methods and methods for improving classification accuracy, including ensemble methods and how to handle imbalanced data. Chapter 9 discusses advanced methods for classification, including Bayesian belief networks, the neural network technique of backpropagation, support vector machines, classification using frequent patterns, k-nearest-neighbor classifiers, case-based reasoning, genetic algorithms, rough set theory, and fuzzy set approaches. Additional topics include multiclass classification, semi-supervised classification, active learning, and transfer learning.
Cluster analysis forms the topic of Chapter 10 and Chapter 11. Chapter 10 introduces the basic concepts and methods for data clustering, including an overview of basic cluster analysis methods, partitioning methods, hierarchical methods, density-based methods, and grid-based methods. It also introduces methods for the evaluation of clustering. Chapter 11 discusses advanced methods for clustering, including probabilistic model-based clustering, clustering high-dimensional data, clustering graph and network data, and clustering with constraints.
Chapter 12 is dedicated to outlier detection. It introduces the basic concepts of outliers and outlier analysis and discusses various outlier detection methods from the view of degree of supervision (i.e., supervised, semi-supervised, and unsupervised methods), as well as from the view of approaches (i.e., statistical methods, proximity-based methods, clustering-based methods, and classification-based methods). It also discusses methods for mining contextual and collective outliers, and for outlier detection in high-dimensional data.
Finally, in Chapter 13, we discuss trends, applications, and research frontiers in data mining. We briefly cover mining complex data types, including mining sequence data (e.g., time series, symbolic sequences, and biological sequences), mining graphs and networks, and mining spatial, multimedia, text, and Web data. In-depth treatment of data mining methods for such data is left to a book on advanced topics in data mining, the writing of which is in progress. The chapter then moves ahead to cover other data mining methodologies, including statistical data mining, foundations of data mining, visual and audio data mining, as well as data mining applications. It discusses data mining for financial data analysis, for industries like retail and telecommunication, for use in science and engineering, and for intrusion detection and prevention. It also discusses the relationship between data mining and recommender systems. Because data mining is present in many aspects of daily life, we discuss issues regarding data mining and society, including ubiquitous and invisible data mining, as well as privacy, security, and the social impacts of data mining. We conclude our study by looking at data mining trends.
Throughout the text, italic font is used to emphasize terms that are defined, while bold font is used to highlight or summarize main ideas. Sans serif font is used for reserved words. Bold italic font is used to represent multidimensional quantities.
This book has several strong features that set it apart from other texts on data mining. It presents a very broad yet in-depth coverage of the principles of data mining. The chapters are written to be as self-contained as possible, so they may be read in order of interest by the reader. Advanced chapters offer a larger-scale