TY - BOOK ID - 14302531 TI - Clustering, association and classification AU - Holmes, Dawn E. AU - Jain, L. C. PY - 2012 SN - 3642231659 3642231667 3642430937 PB - Berlin : Springer, DB - UniCat KW - Data mining. KW - Engineering & Applied Sciences KW - Computer Science KW - Association rule mining. KW - Classification rule mining. KW - CRM (Classification rule mining) KW - Mining, Classification rule KW - Association mining KW - Association rules mining KW - Mining, Association rule KW - Algorithmic knowledge discovery KW - Factual data analysis KW - KDD (Information retrieval) KW - Knowledge discovery in data KW - Knowledge discovery in databases KW - Mining, Data KW - Engineering. KW - Artificial intelligence. KW - Computational intelligence. KW - Computational Intelligence. KW - Artificial Intelligence (incl. Robotics). KW - Intelligence, Computational KW - Artificial intelligence KW - AI (Artificial intelligence) KW - Artificial thinking KW - Electronic brains KW - Intellectronics KW - Intelligence, Artificial KW - Intelligent machines KW - Machine intelligence KW - Thinking, Artificial KW - Bionics KW - Cognitive science KW - Digital computer simulation KW - Electronic data processing KW - Logic machines KW - Machine theory KW - Self-organizing systems KW - Simulation methods KW - Fifth generation computers KW - Neural computers KW - Construction KW - Industrial arts KW - Technology KW - Soft computing KW - Data mining KW - Database searching KW - Artificial Intelligence. UR - https://www.unicat.be/uniCat?func=search&query=sysid:14302531 AB - Data mining is one of the most rapidly growing research areas in computer science and statistics. In Volume 1of this three volume series, we have brought together contributions from some of the most prestigious researchers in the fundamental data mining tasks of clustering, association and classification. Each of the chapters is self contained. Theoreticians and applied scientists/ engineers will find this volume valuable. Additionally, it provides a sourcebook for graduate students interested in the current direction of research in these aspects of data mining. ER -