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006			m o d
007			cr cnu|||unuuu
008			170210s2017 sz a ob 001 0 eng d
020			_a3319527517 _q(electronic bk.)
020			_a9783319527512 _q(electronic bk.)
020			_z3319527509
020			_z9783319527505 _q(print)
035			_a(OCoLC)972093142 _z(OCoLC)974466484 _z(OCoLC)981888724 _z(OCoLC)1005759169 _z(OCoLC)1011903131
040			_aGW5XE _cGW5XE _dYDX _dOCLCF _dNJR _dUAB _dCOO _dIOG _dAZU _dUWO _dVT2 _dUPM _dESU _dZ5A _dJBG _dIAD _dICW _dICN _dOTZ _dOCLCQ _dU3W _dES-MaUEC _bspa
050		4	_aQA76.9.D343 _bH665 2017 EB
100	1		_aHońko, Piotr, _eautor
245	1	0	_aGranular-relational data mining : _bhow to mine relational data in the paradigm of granular computing? _cPiotr Hońko.
264		1	_aCham, Switzerland _bSpringer _c2017
300			_a1 recurso en línea (xv, 123 páginas) _bilustraciones
336			_aTexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	0		_aStudies in computational intelligence _x1860-949X _vvolume 702
500			_aSpringerLink _bSpringer Engineering eBooks 2017 English+International
504			_aIncluye referencias bibliográficas e índice
505	0		_aPreface -- Chapter 1: Introduction -- Part I: Generalized Related Set Based Approach -- Chapter 2: Information System for Relational Data -- Chapter 3: Properties of Granular-Relational Data Mining Framework -- Chapter 4: Association Discovery and Classification Rule Mining -- Chapter 5: Rough-Granular Computing -- Part II: Description Language Based Approach -- Chapter 6: Compound Information Systems -- Chapter 7: From Granular-Data Mining Framework to its Relational Version -- Chapter 8: Relation-Based Granules -- Chapter 9: Compound Approximation Spaces -- Conclusions -- References -- Index.
520	3		_aThis book provides two general granular computing approaches to mining relational data, the first of which uses abstract descriptions of relational objects to build their granular representation, while the second extends existing granular data mining solutions to a relational case. Both approaches make it possible to perform and improve popular data mining tasks such as classification, clustering, and association discovery. How can different relational data mining tasks best be unified? How can the construction process of relational patterns be simplified? How can richer knowledge from relational data be discovered? All these questions can be answered in the same way: by mining relational data in the paradigm of granular computing! This book will allow readers with previous experience in the field of relational data mining to discover the many benefits of its granular perspective. In turn, those readers familiar with the paradigm of granular computing will find valuable insights on its application to mining relational data. Lastly, the book offers all readers interested in computational intelligence in the broader sense the opportunity to deepen their understanding of the newly emerging field granular-relational data mining.
650		7	_aData mining _2embne _0(OCoLC)fst00887946 _0comprobar BNE20033218554 _9162648
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=http://link.springer.com/10.1007/978-3-319-52751-2 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
988			_aEBOOK, asignarmaterias, EBSPRINGER_2017B
998			_b02/2018 _dz _e- _zSI
999			_c95361 _d95361 _x1