000			03961nam a22004575i 4500
001			978-0-387-34296-2
003			DE-He213
005			20250710083953.0
007			cr nn 008mamaa
008			100301s2006 xxu| s |||| 0|eng d
020			_a9780387342962 _a99780387342962
024	7		_a10.1007/0-387-34296-6 _2doi
082	0	4	_a025.04 _223
100	1		_aTriantaphyllou, Evangelos. _eeditor.
245	1	0	_aData Mining and Knowledge Discovery Approaches Based on Rule Induction Techniques _h[recurso electrónico] / _cedited by Evangelos Triantaphyllou, Giovanni Felici.
264		1	_aBoston, MA : _bSpringer US, _c2006.
300			_aXLVIII, 748 p. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aMassive Computing, _x0924-6703 ; _v6
505	0		_aA Common Logic Approach to Data Mining and Pattern Recognition -- The One Clause at a Time (OCAT) Approach to Data Mining and Knowledge Discovery -- An Incremental Learning Algorithm for Inferring Logical Rules from Examples in the Framework of the Common Reasoning Process -- Discovering Rules That Govern Monotone Phenomena -- Learning Logic Formulas and Related Error Distributions -- Feature Selection for Data Mining -- Transformation of Rational Data and Set Data to Logic Data -- Data Farming: Concepts and Methods -- Rule Induction Through Discrete Support Vector Decision Trees -- Multi-Attribute Decision Trees and Decision Rules -- Knowledge Acquisition and Uncertainty in Fault Diagnosis: A Rough Sets Perspective -- Discovering Knowledge Nuggets with a Genetic Algorithm -- Diversity Mechanisms in Pitt-Style Evolutionary Classifier Systems -- Fuzzy Logic in Discovering Association Rules: An Overview -- Mining Human Interpretable Knowledge with Fuzzy Modeling Methods: An Overview -- Data Mining from Multimedia Patient Records -- Learning to Find Context Based Spelling Errors -- Induction and Inference with Fuzzy Rules for Textual Information Retrieval -- Statistical Rule Induction in the Presence of Prior Information: The Bayesian Record Linkage Problem -- Some Future Trends in Data Mining.
520			_aThis book will give the reader a perspective into the core theory and practice of data mining and knowledge discovery (DM&KD). Its chapters combine many theoretical foundations for various DM&KD methods, and they present a rich array of examples-many of which are drawn from real-life applications. Most of the theoretical developments discussed are accompanied by an extensive empirical analysis, which should give the reader both a deep theoretical and practical insight into the subjects covered. The book presents the combined research experiences of its 40 authors gathered during a long search in gleaning new knowledge from data. The last page of each chapter has a brief biographical statement of its contributors, who are world-renowned experts. Audience The intended audience for this book includes graduate students studying data mining who have some background in mathematical logic and discrete optimization, as well as researchers and practitioners in the same area.
650		0	_aCOMPUTER SCIENCE.
650		0	_aINFORMATION STORAGE AND RETRIEVAL SYSTEMS.
650		0	_aOPERATIONS RESEARCH.
650	1	4	_aCOMPUTER SCIENCE.
650	2	4	_aINFORMATION STORAGE AND RETRIEVAL.
650	2	4	_aOPERATIONS RESEARCH, MATHEMATICAL PROGRAMMING.
650	2	4	_aOPERATIONS RESEARCH/DECISION THEORY.
700	1		_aFelici, Giovanni. _eeditor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9780387342948
830		0	_aMassive Computing, _x0924-6703 ; _v6
856	4	0	_uhttp://dx.doi.org/10.1007/0-387-34296-6 _zVer el texto completo en las instalaciones del CICY
912			_aZDB-2-SCS
942			_2ddc _cER
999			_c57363 _d57363