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Mining Heterogeneous Information Networks (Record no. 84956)

000 -LEADER
fixed length control field 04067nam a22005175i 4500
001 - CONTROL NUMBER
control field 978-3-031-01902-9
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20240730163746.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 220601s2012 sz | s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9783031019029
-- 978-3-031-01902-9
082 04 - CLASSIFICATION NUMBER
Call Number 006.312
100 1# - AUTHOR NAME
Author Sun, Yizhou.
245 10 - TITLE STATEMENT
Title Mining Heterogeneous Information Networks
Sub Title Principles and Methodologies /
250 ## - EDITION STATEMENT
Edition statement 1st ed. 2012.
300 ## - PHYSICAL DESCRIPTION
Number of Pages XI, 196 p.
490 1# - SERIES STATEMENT
Series statement Synthesis Lectures on Data Mining and Knowledge Discovery,
505 0# - FORMATTED CONTENTS NOTE
Remark 2 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.
520 ## - SUMMARY, ETC.
Summary, etc Real-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.
700 1# - AUTHOR 2
Author 2 Han, Jiawei.
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier https://doi.org/10.1007/978-3-031-01902-9
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type eBooks
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-- Cham :
-- Springer International Publishing :
-- Imprint: Springer,
-- 2012.
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-- txt
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-- computer
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-- rdamedia
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-- online resource
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-- text file
-- PDF
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650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Data mining.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Statistics .
650 14 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Data Mining and Knowledge Discovery.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Statistics.
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE
-- 2151-0075
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