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Tag clustering algorithm LMMSK: improved K-means algorithm based on latent semantic analysis
被引量：
7
1
作者
Jing Yang
Jun Wang
《Journal of Systems Engineering and Electronics》
SCIE
EI
CSCD
2017年第2期374-384,共11页
With the wide application of Web-2.0 and social software, there are more and more tag-related studies and applications. Because of the randomness and the personalization in users' tagging, tag research continues t...
With the wide application of Web-2.0 and social software, there are more and more tag-related studies and applications. Because of the randomness and the personalization in users' tagging, tag research continues to encounter data space and semantics obstacles. With the min-max similarity (MMS) to establish the initial centroids, the traditional K-means clustering algorithm is firstly improved to the MMSK-means clustering algorithm, the superiority of which has been tested; based on MMSK-means and combined with latent semantic analysis (LSA), here secondly emerges a new tag clustering algorithm, LMMSK. Finally, three algorithms for tag clustering, MMSK-means, tag clustering based on LSA (LSA-based algorithm) and LMMSK, have been run on Matlab, using a real tag-resource dataset obtained from the Delicious Social Bookmarking System from 2004 to 2009. LMMSK's clustering result turns out to be the most effective and the most accurate. Thus, a better tag-clustering algorithm is found for greater application of social tags in personalized search, topic identification or knowledge community discovery. In addition, for a better comparison of the clustering results, the clustering corresponding results matrix (CCR matrix) is proposed, which is promisingly expected to be an effective tool to capture the evolutions of the social tagging system. © 2017 Beijing Institute of Aerospace Information.
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关键词
application
programs
Data
mining
MATLAB
SEMANTICS
Social
networking
(online)
WEBSITES
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题名
Tag clustering algorithm LMMSK: improved K-means algorithm based on latent semantic analysis
被引量：
7
1
作者
Jing Yang
Jun Wang
机构
School of Economics and Management
出处
《Journal of Systems Engineering and Electronics》
SCIE
EI
CSCD
2017年第2期374-384,共11页
基金
supported by the National Natural Science Foundation of China(71271018
71531001)
文摘
With the wide application of Web-2.0 and social software, there are more and more tag-related studies and applications. Because of the randomness and the personalization in users' tagging, tag research continues to encounter data space and semantics obstacles. With the min-max similarity (MMS) to establish the initial centroids, the traditional K-means clustering algorithm is firstly improved to the MMSK-means clustering algorithm, the superiority of which has been tested; based on MMSK-means and combined with latent semantic analysis (LSA), here secondly emerges a new tag clustering algorithm, LMMSK. Finally, three algorithms for tag clustering, MMSK-means, tag clustering based on LSA (LSA-based algorithm) and LMMSK, have been run on Matlab, using a real tag-resource dataset obtained from the Delicious Social Bookmarking System from 2004 to 2009. LMMSK's clustering result turns out to be the most effective and the most accurate. Thus, a better tag-clustering algorithm is found for greater application of social tags in personalized search, topic identification or knowledge community discovery. In addition, for a better comparison of the clustering results, the clustering corresponding results matrix (CCR matrix) is proposed, which is promisingly expected to be an effective tool to capture the evolutions of the social tagging system. © 2017 Beijing Institute of Aerospace Information.
关键词
application
programs
Data
mining
MATLAB
SEMANTICS
Social
networking
(online)
WEBSITES
Keywords
application programs
Data mining
MATLAB
Semantics
Social networking (online)
Websites
分类号
TP311.13 [自动化与计算机技术—计算机软件与理论]
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Tag clustering algorithm LMMSK: improved K-means algorithm based on latent semantic analysis
Jing Yang
Jun Wang
《Journal of Systems Engineering and Electronics》
SCIE
EI
CSCD
2017
7
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