000			03471nam a22003735i 4500
001			309537
003			MX-SnUAN
005			20160429160307.0
007			cr nn 008mamaa
008			150903s2007 sz | o |||| 0|eng d
020			_a9783764379889 _99783764379889
024	7		_a10.1007/9783764379889 _2doi
035			_avtls000362849
039		9	_a201509030649 _bVLOAD _c201405070335 _dVLOAD _y201402211059 _zstaff
040			_aMX-SnUAN _bspa _cMX-SnUAN _erda
050		4	_aT57-57.97
100	1		_aAbonyi, János. _eautor _9315048
245	1	0	_aCluster Analysis for Data Mining and System Identification / _cby János Abonyi, Balázs Feil.
264		1	_aBasel : _bBirkhäuser Basel, _c2007.
300			_axviii, 303 páginas 120 ilustraciones _brecurso en línea.
336			_atexto _btxt _2rdacontent
337			_acomputadora _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_aarchivo de texto _bPDF _2rda
500			_aSpringer eBooks
505	0		_aClassical Fuzzy Cluster Analysis -- Visualization of the Clustering Results -- Clustering for Fuzzy Model Identification — Regression -- Fuzzy Clustering for System Identification -- Fuzzy Model based Classifiers -- Segmentation of Multivariate Time-series.
520			_aThis book presents new approaches to data mining and system identification. Algorithms that can be used for the clustering of data have been overviewed. New techniques and tools are presented for the clustering, classification, regression and visualization of complex datasets. Special attention is given to the analysis of historical process data, tailored algorithms are presented for the data driven modeling of dynamical systems, determining the model order of nonlinear input-output black box models, and the segmentation of multivariate time-series. The main methods and techniques are illustrated through several simulated and real-world applications from data mining and process engineering practice. The book is aimed primarily at practitioners, researches, and professionals in statistics, data mining, business intelligence, and systems engineering, but it is also accessible to graduate and undergraduate students in applied mathematics, computer science, electrical and process engineering. Familiarity with the basics of system identification and fuzzy systems is helpful but not required. Key features: - Detailed overview of the most powerful algorithms and approaches for data mining and system identification is presented. - Extensive references give a good overview of the current state of the application of computational intelligence in data mining and system identification, and suggest further reading for additional research. - Numerous illustrations to facilitate the understanding of ideas and methods presented. - Supporting MATLAB files, available at the website www.fmt.uni-pannon.hu/softcomp create a computational platform for exploration and illustration of many concepts and algorithms presented in the book.
590			_aPara consulta fuera de la UANL se requiere clave de acceso remoto.
700	1		_aFeil, Balázs. _eautor _9349823
710	2		_aSpringerLink (Servicio en línea) _9299170
776	0	8	_iEdición impresa: _z9783764379872
856	4	0	_uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-3-7643-7988-9 _zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
942			_c14
999			_c309537 _d309537