"Data Mining: Principles and Practice" is a textbook that combines theory with practice and describes the theory, technology and application of data mining. Discussed all aspects of data mining, from basic theories to complex data types and their applications. It not only discusses traditional data mining problems, but also introduces advanced data types such as text, time series, discrete series, spatial data, graph data, and social networks. "Data Mining: Principles and Practice" consists of basic and advanced chapters. The basic chapter corresponds to chapters 1 to 11 of the original book, and the advanced chapter corresponds to chapters 12 to 20 of the original book.
Basic chapters: Data mining mainly has four "super problems", namely clustering, classification, association pattern mining, and anomaly analysis. Their importance is reflected in the fact that many practical applications regard them as basic components. Therefore, data mining researchers and practitioners attach great importance to designing effective and efficient methods for these problems. These basic chapters discuss in detail the various solutions proposed by the data mining field for these super problems.
Domain chapters: These chapters discuss special methods in different domains, including text data, time series data, sequence data, graph data, spatial data, etc. Most of these chapters can be considered as application chapters, because they explore the specific problems of a particular field.
Application chapter: The development of computer hardware technology and software platform has led to the production of some data-intensive applications, such as data streaming systems, Web mining, social networks and privacy protection. The application chapter gives a detailed introduction to these topics. In fact, those domain chapters mentioned above also focused on various applications generated by these different data types.
The Chinese version of "Data Mining: Principles and Practice" is divided into basic and advanced chapters, in-depth discussion of all aspects of data mining, from basic knowledge to complex data types and applications, and capture various problem areas of data mining. It not only focuses on traditional data mining problems, but also introduces advanced data types, such as text, time series, discrete series, spatial data, graph data, and social network data. So far, no book has explored all these topics in such a comprehensive and comprehensive manner.
Fundamentals: Detailed introduction to various solutions to the four main problems of data mining (clustering, classification, association pattern mining and anomaly analysis), specific mining methods used in the field of text data, and applications for data stream mining .
Advanced: Mainly discusses specific mining methods used in different data fields (such as time series data, sequence data, spatial data, graph data), and important data mining applications (such as web data mining, ranking, recommendation, social network analysis) And privacy protection).
"Data Mining: Principles and Practice (Basics)"
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"Data Mining: Principles and Practice (Advanced)"
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mobile version
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