Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 이기천 | - |
dc.date.accessioned | 2018-03-16T06:44:08Z | - |
dc.date.available | 2018-03-16T06:44:08Z | - |
dc.date.issued | 2014-04 | - |
dc.identifier.citation | Knowledge-Based Systems, 2014, 60, P.58-72 | en_US |
dc.identifier.issn | 0950-7051 | - |
dc.identifier.issn | 1872-7409 | - |
dc.identifier.uri | http://www.sciencedirect.com/science/article/pii/S095070511400015X | - |
dc.identifier.uri | http://hdl.handle.net/20.500.11754/48005 | - |
dc.description.abstract | We propose a clustering method maximizing a new measure called "group dependence." Group dependence quantifies how precise a certain division of a graph is in terms of dependence distance. Built upon statistical dependence measure between points driven by Markovian transitions, group dependence incorporates the geometric structure of input data. Besides capturing degrees of positive dependence and coherence for a group division, group dependence inherently supplies the proposed clustering method with a definite decision on the depth of division. We provide an optimality aspect of the method as theoretical justification in consideration of posterior transition probabilities of input data. Illustrating its procedure using data from a known structure, we demonstrate its performance in the clustering task of real-world data sets, Amazon, DBLP, and YouTube, in comparison with selected clustering algorithms. We show that the proposed method outperforms the selected methods in reasonable settings: in particular, the proposed method surpasses modularity clustering in terms of normalized mutual information. We also show that the proposed method reveals additional insights on community structure detection according to its connectivity scale parameter. (C) 2014 Elsevier B.V. All rights reserved. | en_US |
dc.description.sponsorship | This research was supported by a grant from the R&D Program (Industrial Strategic Technology Development) funded by the Ministry of Knowledge Economy (MKE), Republic of Korea: the Grant No. 10042693 and the grant title Socio-Cognitive Design Technology for Convergence Service. Also, the authors are deeply thankful to all interested persons of MIKE and KEIT (Korea Evaluation Institute of Industrial Technology). | en_US |
dc.language.iso | en | en_US |
dc.publisher | Elsevier Science B.V | en_US |
dc.subject | Group dependence | en_US |
dc.subject | Clustering; Markovian | en_US |
dc.subject | Community structure | en_US |
dc.subject | Mutual information | en_US |
dc.title | Dependence clustering, a method revealing community structure with group dependence | en_US |
dc.type | Article | en_US |
dc.relation.volume | 60 | - |
dc.identifier.doi | 10.1016/j.knosys.2014.01.004 | - |
dc.relation.page | 58-72 | - |
dc.relation.journal | KNOWLEDGE-BASED SYSTEMS | - |
dc.contributor.googleauthor | Park, H. | - |
dc.contributor.googleauthor | Lee, K. | - |
dc.relation.code | 2014035204 | - |
dc.sector.campus | S | - |
dc.sector.daehak | COLLEGE OF ENGINEERING[S] | - |
dc.sector.department | DEPARTMENT OF INDUSTRIAL ENGINEERING | - |
dc.identifier.pid | skylee | - |
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