000			02302nam a22002177a 4500
999			_c1563 _d1563
005			20220322113443.0
008			220322b ||||| |||| 00| 0 eng d
020			_a9783319381169
082			_a006.312 _bAGG
100			_aAggarwal, Charu C. _94493
245			_aData mining: the textbook
260			_bSpringer _aSwitzerland _c2015
300			_axxix, 734 p.
365			_aEURO _b49.99
520			_aIntroduction This textbook explores the different aspects of data mining from the fundamentals to the complex data types and their applications, capturing the wide diversity of problem domains for data mining issues. It goes beyond the traditional focus on data mining problems to introduce advanced data types such as text, time series, discrete sequences, spatial data, graph data, and social networks. Until now, no single book has addressed all these topics in a comprehensive and integrated way. The chapters of this book fall into one of three categories: Fundamental chapters: Data mining has four main problems, which correspond to clustering, classification, association pattern mining, and outlier analysis. These chapters comprehensively discuss a wide variety of methods for these problems. Domain chapters: These chapters discuss the specific methods used for different domains of data such as text data, time-series data, sequence data, graph data, and spatial data. Application chapters: These chapters study important applications such as stream mining, Web mining, ranking, recommendations, social networks, and privacy preservation. The domain chapters also have an applied flavor. Appropriate for both introductory and advanced data mining courses, Data Mining: The Textbook balances mathematical details and intuition. It contains the necessary mathematical details for professors and researchers, but it is presented in a simple and intuitive style to improve accessibility for students and industrial practitioners (including those with a limited mathematical background). Numerous illustrations, examples, and exercises are included, with an emphasis on semantically interpretable examples.
650			_aData mining _9365
650			_aComputer science _91018
650			_aOptical pattern recognition _96097
650			_aPattern perception _96098
942			_2ddc _cBK