000 04067nam a22005175i 4500
001 978-3-031-01902-9
003 DE-He213
005 20240730163746.0
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008 220601s2012 sz | s |||| 0|eng d
020 _a9783031019029
_9978-3-031-01902-9
024 7 _a10.1007/978-3-031-01902-9
_2doi
050 4 _aQA76.9.D343
072 7 _aUNF
_2bicssc
072 7 _aUYQE
_2bicssc
072 7 _aCOM021030
_2bisacsh
072 7 _aUNF
_2thema
072 7 _aUYQE
_2thema
082 0 4 _a006.312
_223
100 1 _aSun, Yizhou.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_980413
245 1 0 _aMining Heterogeneous Information Networks
_h[electronic resource] :
_bPrinciples and Methodologies /
_cby Yizhou Sun, Jiawei Han.
250 _a1st ed. 2012.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2012.
300 _aXI, 196 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _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.
650 0 _aData mining.
_93907
650 0 _aStatistics .
_931616
650 1 4 _aData Mining and Knowledge Discovery.
_980414
650 2 4 _aStatistics.
_914134
700 1 _aHan, Jiawei.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_92109
710 2 _aSpringerLink (Online service)
_980415
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031007743
776 0 8 _iPrinted edition:
_z9783031030307
830 0 _aSynthesis Lectures on Data Mining and Knowledge Discovery,
_x2151-0075
_980416
856 4 0 _uhttps://doi.org/10.1007/978-3-031-01902-9
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942 _cEBK
999 _c84956
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