Issue No. 12 - Dec. (2013 vol. 25)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2012.194
Hongyan Liu , Tsinghua University, Beijing
Jun He , Renmin University of China, Beijing
Dan Zhu , Renmin University of China, Beijing
Charles X. Ling , The University of Western Ontario, Ontario
Xiaoyong Du , Renmin University of China, Beijing
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.
Current measurement, Atmospheric measurements, Particle measurements, Approximation algorithms, Algorithm design and analysis, Computational modeling, Data mining
H. Liu, J. He, D. Zhu, C. X. Ling and X. Du, "Measuring Similarity Based on Link Information: A Comparative Study," in IEEE Transactions on Knowledge & Data Engineering, vol. 25, no. 12, pp. 2823-2840, 2013.