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Measuring Similarity Based on Link Information: A Comparative Study.

Authors :
Liu, Hongyan
He, Jun
Zhu, Dan
Ling, Charles X.
Du, Xiaoyong
Source :
IEEE Transactions on Knowledge & Data Engineering. Dec2013, Vol. 25 Issue 12, p2823-2840. 18p.
Publication Year :
2013

Abstract

Measuring similarity between objects is a fundamental task in domains such as data mining, information retrieval, and so on. Link-based similarity measures have attracted the attention of many researchers and have been widely applied in recent years. However, most previous works mainly focus on introducing new link-based measures, and seldom provide theoretical as well as experimental comparisons with other measures. Thus, selecting the suitable measure in different situations and applications is difficult. In this paper, a comprehensive analysis and critical comparison of various link-based similarity measures and algorithms are presented. Their strengths and weaknesses are discussed. Their actual runtime performances are also compared via experiments on benchmark data sets. Some novel and useful guidelines for users to choose the appropriate link-based measure for their applications are discovered. [ABSTRACT FROM PUBLISHER]

Details

Language :
English
ISSN :
10414347
Volume :
25
Issue :
12
Database :
Academic Search Index
Journal :
IEEE Transactions on Knowledge & Data Engineering
Publication Type :
Academic Journal
Accession number :
91789804
Full Text :
https://doi.org/10.1109/TKDE.2012.194