Grouping people in social networks using a weighted multi-constraints clustering method
Alsaleh, Slah & Nayak, Richi (2012) Grouping people in social networks using a weighted multi-constraints clustering method. In Proceedings of the 2012 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), IEEE, Brisbane, QLD.
Grouping users in social networks is an important process that improves matching and recommendation activities in social networks. The data mining methods of clustering can be used in grouping the users in social networks. However, the existing general purpose clustering algorithms perform poorly on the social network data due to the special nature of users' data in social networks. One main reason is the constraints that need to be considered in grouping users in social networks. Another reason is the need of capturing large amount of information about users which imposes computational complexity to an algorithm. In this paper, we propose a scalable and effective constraint-based clustering algorithm based on a global similarity measure that takes into consideration the users' constraints and their importance in social networks. Each constraint's importance is calculated based on the occurrence of this constraint in the dataset. Performance of the algorithm is demonstrated on a dataset obtained from an online dating website using internal and external evaluation measures. Results show that the proposed algorithm is able to increases the accuracy of matching users in social networks by 10% in comparison to other algorithms.
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|Item Type:||Conference Paper|
|Keywords:||Clustering users in social network, social matching system , constraints clustering|
|Subjects:||Australian and New Zealand Standard Research Classification > ENGINEERING (090000)|
|Divisions:||Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
|Copyright Owner:||Copyright 2012 IEEE|
|Deposited On:||19 Feb 2013 23:05|
|Last Modified:||12 Jun 2013 15:32|
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