| 000 -LEADER |
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07931nam a2200745 i 4500 |
| 001 - CONTROL NUMBER |
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| control field |
6812698 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
IEEE |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20200413152906.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS |
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m eo d |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
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cr cn |||m|||a |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
120817s2012 caua foab 000 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9781608458813 (electronic bk.) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| Canceled/invalid ISBN |
9781608458806 (pbk.) |
| 024 7# - OTHER STANDARD IDENTIFIER |
|---|
| Standard number or code |
10.2200/S00433ED1V01Y201207DMK005 |
| Source of number or code |
doi |
| 035 ## - SYSTEM CONTROL NUMBER |
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| System control number |
(CaBNVSL)swl00401119 |
| 035 ## - SYSTEM CONTROL NUMBER |
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| System control number |
(OCoLC)801595907 |
| 040 ## - CATALOGING SOURCE |
|---|
| Original cataloging agency |
CaBNVSL |
| Transcribing agency |
CaBNVSL |
| Modifying agency |
CaBNVSL |
| 050 #4 - LIBRARY OF CONGRESS CALL NUMBER |
|---|
| Classification number |
QA76.9.D343 |
| Item number |
S866 2012 |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
006.3 |
| Edition number |
23 |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Sun, Yizhou. |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Mining heterogeneous information networks |
| Medium |
[electronic resource] : |
| Remainder of title |
principles and methodologies / |
| Statement of responsibility, etc. |
Yizhou Sun and Jiawei Han. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
|---|
| Place of publication, distribution, etc. |
San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA) : |
| Name of publisher, distributor, etc. |
Morgan & Claypool, |
| Date of publication, distribution, etc. |
c2012. |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
1 electronic text (xi, 147 p.) : |
| Other physical details |
ill., digital file. |
| 490 1# - SERIES STATEMENT |
|---|
| Series statement |
Synthesis lectures on data mining and knowledge discovery, |
| International Standard Serial Number |
2151-0075 ; |
| Volume/sequential designation |
# 5 |
| 538 ## - SYSTEM DETAILS NOTE |
|---|
| System details note |
Mode of access: World Wide Web. |
| 538 ## - SYSTEM DETAILS NOTE |
|---|
| System details note |
System requirements: Adobe Acrobat Reader. |
| 500 ## - GENERAL NOTE |
|---|
| General note |
Part of: Synthesis digital library of engineering and computer science. |
| 500 ## - GENERAL NOTE |
|---|
| General note |
Series from website. |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc. note |
Includes bibliographical references (p. 139-146). |
| 505 0# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
1. Introduction -- 1.1 What are heterogeneous information networks? -- 1.2 Why is mining heterogeneous networks a new game? -- 1.3 Organization of the book -- |
| 505 8# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
Part I. Ranking-based clustering and classification -- 2. Ranking-based clustering -- 2.1 Overview -- 2.2 RankClus -- 2.2.1 Ranking functions -- 2.2.2 From conditional rank distributions to new clustering measures -- 2.2.3 Cluster centers and distance measure -- 2.2.4 RankClus: algorithm summarization -- 2.2.5 Experimental results -- 2.3 NetClus -- 2.3.1 Ranking functions -- 2.3.2 Framework of NetClus algorithm -- 2.3.3 Generative model for target objects in a net-cluster -- 2.3.4 Posterior probability for target objects and attribute objects -- 2.3.5 Experimental results -- |
| 505 8# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
3. Classification of heterogeneous information networks / Ming Ji -- 3.1 Overview -- 3.2 GNetMine -- 3.2.1 The classification problem definition -- 3.2.2 Graph-based regularization framework -- 3.3 RankClass -- 3.3.1 The framework of RankClass -- 3.3.2 Graph-based ranking -- 3.3.3 Adjusting the network -- 3.3.4 Posterior probability calculation -- 3.4 Experimental results -- 3.4.1 Dataset -- 3.4.2 Accuracy study -- 3.4.3 Case study -- |
| 505 8# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
Part II. Meta-path-based similarity search and mining -- 4. Meta-path-based similarity search -- 4.1 Overview -- 4.2 PathSim: a meta-path-based similarity measure -- 4.2.1 Network schema and meta-path -- 4.2.2 Meta-path-based similarity framework -- 4.2.3 PathSim: a novel similarity measure -- 4.3 Online query processing for single meta-path -- 4.3.1 Single meta-path concatenation -- 4.3.2 Baseline -- 4.3.3 Co-clustering-based pruning -- 4.4 Multiple meta-paths combination -- 4.5 Experimental results -- 4.5.1 Effectiveness -- 4.5.2 Efficiency comparison -- 4.5.3 Case-study on Flickr network -- |
| 505 8# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
5. Meta-path-based relationship prediction -- 5.1 Overview -- 5.2 Meta-path-based relationship prediction framework -- 5.2.1 Meta-path-based topological feature space -- 5.2.2 Supervised relationship prediction framework -- 5.3 Co-authorship prediction -- 5.3.1 The co-authorship prediction model -- 5.3.2 Experimental results -- 5.4 Relationship prediction with time -- 5.4.1 Meta-path-based topological features for author citation relationship prediction -- 5.4.2 The relationship building time prediction model -- 5.4.3 Experimental results -- |
| 505 8# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
Part III. Relation strength-aware mining -- 6. Relation strength-aware clustering with incomplete attributes -- 6.1 Overview -- 6.2 The relation strength-aware clustering problem definition -- 6.2.1 The clustering problem -- 6.3 The clustering framework -- 6.3.1 Model overview -- 6.3.2 Modeling attribute generation -- 6.3.3 Modeling structural consistency -- 6.3.4 The unified model -- 6.4 The clustering algorithm -- 6.4.1 Cluster optimization -- 6.4.2 Link type strength learning -- 6.4.3 Putting together: the GenClus algorithm -- 6.5 Experimental results -- 6.5.1 Datasets -- 6.5.2 Effectiveness study -- |
| 505 8# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
7. User-guided clustering via meta-path selection -- 7.1 Overview -- 7.2 The meta-path selection problem for user-guided clustering -- 7.2.1 The meta-path selection problem -- 7.2.2 User-guided clustering -- 7.2.3 The problem definition -- 7.3 The probabilistic model -- 7.3.1 Modeling the relationship generation -- 7.3.2 Modeling the guidance from users -- 7.3.3 Modeling the quality weights for meta-path selection -- 7.3.4 The unified model -- 7.4 The learning algorithm -- 7.4.1 Optimize clustering result given meta-path weights -- 7.4.2 Optimize meta-path weights given clustering result -- 7.4.3 The PathSelClus algorithm -- 7.5 Experimental results -- 7.5.1 Datasets -- 7.5.2 Effectiveness study -- 7.5.3 Case study on meta-path weights -- 7.6 Discussions -- |
| 505 8# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
8. Research frontiers -- Bibliography -- Authors' biographies. |
| 506 1# - RESTRICTIONS ON ACCESS NOTE |
|---|
| Terms governing access |
Abstract freely available; full-text restricted to subscribers or individual document purchasers. |
| 510 0# - CITATION/REFERENCES NOTE |
|---|
| Name of source |
Compendex |
| 510 0# - CITATION/REFERENCES NOTE |
|---|
| Name of source |
INSPEC |
| 510 0# - CITATION/REFERENCES NOTE |
|---|
| Name of source |
Google scholar |
| 510 0# - CITATION/REFERENCES NOTE |
|---|
| Name of source |
Google book search |
| 520 3# - 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. |
| 530 ## - ADDITIONAL PHYSICAL FORM AVAILABLE NOTE |
|---|
| Additional physical form available note |
Also available in print. |
| 588 ## - SOURCE OF DESCRIPTION NOTE |
|---|
| Source of description note |
Title from PDF t.p. (viewed on August 17, 2012). |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Data mining. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Information networks. |
| 653 ## - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
information network mining |
| 653 ## - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
heterogeneous information networks |
| 653 ## - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
link analysis |
| 653 ## - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
clustering |
| 653 ## - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
classification |
| 653 ## - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
ranking |
| 653 ## - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
similarity search |
| 653 ## - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
relationship prediction |
| 653 ## - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
user-guided clustering |
| 653 ## - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
probabilistic models |
| 653 ## - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
real-world applications |
| 653 ## - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
efficient and scalable algorithms |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Han, Jiawei. |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY |
|---|
| Relationship information |
Print version: |
| International Standard Book Number |
9781608458806 |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE |
|---|
| Uniform title |
Synthesis digital library of engineering and computer science. |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE |
|---|
| Uniform title |
Synthesis lectures on data mining and knowledge discovery ; |
| Volume/sequential designation |
# 5. |
| International Standard Serial Number |
2151-0075 |
| 856 42 - ELECTRONIC LOCATION AND ACCESS |
|---|
| Materials specified |
Abstract with links to resource |
| Uniform Resource Identifier |
http://ieeexplore.ieee.org/servlet/opac?bknumber=6812698 |
