Clustering algorithms for tags

Zong, Y; Xu, G

Clustering algorithms for tags

Zong, Y Xu, G

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Publication Type:: Chapter
Citation:: Social Media Mining and Social Network Analysis: Emerging Research, 2013, pp. 39 - 53
Issue Date:: 2013-12-01

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Field	Value	Language
dc.contributor.author	Zong, Y	en_US
dc.contributor.author	Xu, G https://orcid.org/0000-0003-4493-6663	en_US
dc.date.issued	2013-12-01	en_US
dc.identifier.citation	Social Media Mining and Social Network Analysis: Emerging Research, 2013, pp. 39 - 53	en_US
dc.identifier.isbn	9781466628069	en_US
dc.identifier.uri	http://hdl.handle.net/10453/26293
dc.identifier.uri	http://hdl.handle.net/10453/35447
dc.description.abstract	With the development and application of social media, more and more user-generated contents are created. Tag data, a kind of typical user generated content, has attracted lots of interests of researchers. In general, tags are the freely chosen textual descriptions by users to label digital data sources in social tagging systems. Poor retrieval performance remains a major problem of most social tagging systems resulting from the severe difficulty of ambiguity, redundancy, and less semantic nature of tags. Clustering method is a useful tool to increase the ability of information retrieval in the aforementioned systems. In this chapter, the authors (1) review the background of state-of-the-art tagging clustering and the tag data description, (2) present five kinds of tag similarity measurements proposed by researchers, and (3) finally propose a new clustering algorithm for tags based on local information that is derived from Kernel function. This chapter aims to benefit both academic and industry communities who are interested in the techniques and applications of tagging clustering. © 2013, IGI Global.	en_US
dc.relation.ispartof	Social Media Mining and Social Network Analysis: Emerging Research	en_US
dc.relation.isbasedon	10.4018/978-1-4666-2806-9.ch003	en_US
dc.title	Clustering algorithms for tags	en_US
dc.type	Chapter
utslib.for	080609 Information Systems Management	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

With the development and application of social media, more and more user-generated contents are created. Tag data, a kind of typical user generated content, has attracted lots of interests of researchers. In general, tags are the freely chosen textual descriptions by users to label digital data sources in social tagging systems. Poor retrieval performance remains a major problem of most social tagging systems resulting from the severe difficulty of ambiguity, redundancy, and less semantic nature of tags. Clustering method is a useful tool to increase the ability of information retrieval in the aforementioned systems. In this chapter, the authors (1) review the background of state-of-the-art tagging clustering and the tag data description, (2) present five kinds of tag similarity measurements proposed by researchers, and (3) finally propose a new clustering algorithm for tags based on local information that is derived from Kernel function. This chapter aims to benefit both academic and industry communities who are interested in the techniques and applications of tagging clustering. © 2013, IGI Global.

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http://hdl.handle.net/10453/26293