| 000 | 04792nam a22004575i 4500 | ||
|---|---|---|---|
| 001 | 978-3-319-14142-8 | ||
| 003 | DE-He213 | ||
| 005 | 20200421111704.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 150413s2015 gw | s |||| 0|eng d | ||
| 020 |
_a9783319141428 _9978-3-319-14142-8 |
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| 024 | 7 |
_a10.1007/978-3-319-14142-8 _2doi |
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| 050 | 4 | _aQA76.9.D343 | |
| 072 | 7 |
_aUNF _2bicssc |
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_aUYQE _2bicssc |
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_aCOM021030 _2bisacsh |
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| 082 | 0 | 4 |
_a006.312 _223 |
| 100 | 1 |
_aAggarwal, Charu C. _eauthor. |
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| 245 | 1 | 0 |
_aData Mining _h[electronic resource] : _bThe Textbook / _cby Charu C. Aggarwal. |
| 264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2015. |
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| 300 |
_aXXIX, 734 p. 180 illus., 173 illus. in color. _bonline resource. |
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| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bc _2rdamedia |
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| 338 |
_aonline resource _bcr _2rdacarrier |
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| 347 |
_atext file _bPDF _2rda |
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| 505 | 0 | _aIntroduction to Data Mining -- Data Preparation -- Similarity and Distances -- Association Pattern Mining -- Association Pattern Mining: Advanced Concepts -- Cluster Analysis -- Cluster Analysis: Advanced Concepts -- Outlier Analysis -- Outlier Analysis: Advanced Concepts -- Data Classification -- Data Classification: Advanced Concepts -- Mining Data Streams -- Mining Text Data -- Mining Time-Series Data -- Mining Discrete Sequences -- Mining Spatial Data -- Mining Graph Data -- Mining Web Data -- Social Network Analysis -- 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. Praise for Data Mining: The Textbook - "As I read through this book, I have already decided to use it in my classes. This 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. The book is complete with theory and practical use cases. It's 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. 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 | 0 | _aComputer science. | |
| 650 | 0 | _aData mining. | |
| 650 | 0 | _aPattern recognition. | |
| 650 | 1 | 4 | _aComputer Science. |
| 650 | 2 | 4 | _aData Mining and Knowledge Discovery. |
| 650 | 2 | 4 | _aPattern Recognition. |
| 710 | 2 | _aSpringerLink (Online service) | |
| 773 | 0 | _tSpringer eBooks | |
| 776 | 0 | 8 |
_iPrinted edition: _z9783319141411 |
| 856 | 4 | 0 | _uhttp://dx.doi.org/10.1007/978-3-319-14142-8 |
| 912 | _aZDB-2-SCS | ||
| 942 | _cEBK | ||
| 999 |
_c55131 _d55131 |
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