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007			cr nn 008mamaa
008			220601s2012 sz | s |||| 0|eng d
020			_a9783031019029
024	7		_a10.1007/978-3-031-01902-9 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.9.D343 _b2012 EB
100	1		_aSun, Yizhou _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687224
245	1	0	_aMining Heterogeneous Information Networks : _bPrinciples and Methodologies _cby Yizhou Sun, Jiawei Han
250			_a1st edition 2012
264		1	_aCham _bSpringer International Publishing _c2012
300			_a1 recurso en línea (XI, 196 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Data Mining and Knowledge Discovery _x2151-0075
505	0		_aIntroduction -- Ranking-Based Clustering -- Classification of Heterogeneous Information Networks -- Meta-Path-Based Similarity Search -- Meta-Path-Based Relationship Prediction -- Relation Strength-Aware Clustering with Incomplete Attributes -- User-Guided Clustering via Meta-Path Selection -- Research Frontiers.
520			_aReal-world physical and abstract data objects are interconnected, forming gigantic, interconnected networks. By structuring these data objects and interactions between these objects into multiple types, such networks become semi-structured heterogeneous information networks. Most real-world applications that handle big data, including interconnected social media and social networks, scientific, engineering, or medical information systems, online e-commerce systems, and most database systems, can be structured into heterogeneous information networks. Therefore, effective analysis of large-scale heterogeneous information networks poses an interesting but critical challenge. In this book, we investigate the principles and methodologies of mining heterogeneous information networks. Departing from many existing network models that view interconnected data as homogeneous graphs or networks, our semi-structured heterogeneous information network model leverages the rich semantics of typed nodes and links in a network and uncovers surprisingly rich knowledge from the network. This semi-structured heterogeneous network modeling leads to a series of new principles and powerful methodologies for mining interconnected data, including: (1) rank-based clustering and classification; (2) meta-path-based similarity search and mining; (3) relation strength-aware mining, and many other potential developments. This book introduces this new research frontier and points out some promising research directions. Table of Contents: Introduction / Ranking-Based Clustering / Classification of Heterogeneous Information Networks / Meta-Path-Based Similarity Search / Meta-Path-Based Relationship Prediction / Relation Strength-Aware Clustering with Incomplete Attributes / User-Guided Clustering via Meta-Path Selection / Research Frontiers.
988			_aSynthesis Collection of Technology_2012
650		7	_2embne _9162648 _aData mining
650		7	_2embne _9147913 _aRedes de información
700	1		_aHan, Jiawei _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
776	0	8	_iPrinted edition: _z9783031007743
776	0	8	_iPrinted edition: _z9783031030307
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-01902-9 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b03/2023 _dz _esc _zSI