000			02954 a2200205 4500
008			150922b2015 xxu||||| |||| 00| 0 eng d
020			_a9783319141411
082			_a006.312 _bA4D2
100			_aAggarwal, Charu C. _997869
245			_aData mining: the textbook _cAggarwal, Charu C.
260			_bSpringer International Publishing _c2015 _aSwitzerland
300			_axxix, 734 p.
504			_a Table of contents: 1.Introduction to Data Mining 2.Data Preparation 3.Similarity and Distances 4.Association Pattern Mining 5.Association Pattern Mining: Advanced Concepts 6.Cluster Analysis 7.Cluster Analysis: Advanced Concepts 8.Outlier Analysis 9.Outlier Analysis: Advanced Concepts 10.Data Classification 11.Data Classification: Advanced Concepts 12.Mining Data Streams.- Mining Text Data 13.Mining Time-Series Data 14.Mining Discrete Sequences 15.Mining Spatial Data 16.Mining Graph Data 17.Mining Web Data 18.Social Network Analysis 19.Privacy-Preserving Data Mining
520			_aThis 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. --publisher. (http://www.springer.com/gp/book/9783319141411)
650			_aDatabase management
650			_aData mining
650			_aData mining and knowledge discovery
650			_aPattern recognition
942			_2ddc _cBK
999			_c388910 _d388910