000 04365nam a22005295i 4500
001 978-3-7643-7988-9
003 DE-He213
005 20181204132647.0
007 cr nn 008mamaa
008 100301s2007 sz | s |||| 0|eng d
020 _a9783764379889
_9978-3-7643-7988-9
024 7 _a10.1007/978-3-7643-7988-9
_2doi
040 _aISI Library, Kolkata
050 4 _aT57-57.97
072 7 _aPBW
_2bicssc
072 7 _aMAT003000
_2bisacsh
072 7 _aPBW
_2thema
082 0 4 _a519
_223
100 1 _aAbonyi, János.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aCluster Analysis for Data Mining and System Identification
_h[electronic resource] /
_cby János Abonyi, Balázs Feil.
264 1 _aBasel :
_bBirkhäuser Basel,
_c2007.
300 _aXVIII, 306 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
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.
650 0 _aMathematics.
650 0 _aMathematical statistics.
650 0 _aStatistics.
650 1 4 _aApplications of Mathematics.
_0http://scigraph.springernature.com/things/product-market-codes/M13003
650 2 4 _aStatistical Theory and Methods.
_0http://scigraph.springernature.com/things/product-market-codes/S11001
650 2 4 _aStatistics and Computing/Statistics Programs.
_0http://scigraph.springernature.com/things/product-market-codes/S12008
650 2 4 _aStatistics for Business/Economics/Mathematical Finance/Insurance.
_0http://scigraph.springernature.com/things/product-market-codes/S17010
650 2 4 _aStatistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
_0http://scigraph.springernature.com/things/product-market-codes/S17020
650 2 4 _aStatistics for Life Sciences, Medicine, Health Sciences.
_0http://scigraph.springernature.com/things/product-market-codes/S17030
700 1 _aFeil, Balázs.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783764391744
776 0 8 _iPrinted edition:
_z9783764379872
856 4 0 _uhttps://doi.org/10.1007/978-3-7643-7988-9
912 _aZDB-2-SMA
942 _cEB
950 _aMathematics and Statistics (Springer-11649)
999 _c425632
_d425632