000			03942nam a22001817a 4500
020			_a9788319141411
082			_a658.4038 AGG
100			_aAggarwal, Charu C.
245			_aData mining : the textbook
260			_aNew York _bSpringer _c2015
300			_axxv, 734 p. _b25 cm ; Hard
500			_aAlpha 2255/191115/Rs.5403/-
505			_aTable of contents: 1. An 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 13. Mining Text Data 14. Mining time series Data 15. Mining discrete sequences 16. Mining spatial data 17. Mining Graph Data 18. Mining Web Data 19. Social network analysis 20. Privacy –preservation 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. Praise for Data Mining: The Textbook - ℓ́ℓAs I read through this book, I have already decided to use it in my classes. ℗ℓThis is a book written by an outstanding researcher who has made fundamental contributions to data mining, in a way that is both accessible and up to date. ℗ℓThe book is complete with theory and practical use cases. ℗ℓItℓ́ℓs a must-have for students and professors alike!" -- Qiang Yang, Chair of Computer Science and Engineering at Hong Kong University of Science and Technology "This is the most amazing and comprehensive text book on data mining. It covers not only the fundamental problems, such as clustering, classification, outliers and frequent patterns, and different data types, including text, time series, sequences, spatial data and graphs, but also various applications, such as recommenders, Web, social network and privacy.℗ℓ It is a great book for graduate students and researchers as well as practitioners." -- Philip S. Yu, UIC Distinguished Professor and Wexler Chair in Information Technology at University of Illinois at Chicago.
650			_aOptical pattern recognition _aPattern Recognition _aData Mining and Knowledge Discovery
856			_uhttp://www.springer.com/in/book/9783319141411
942			_cBK
999			_c102715 _d102715