Relative validity criteria for community mining algorithms

Rabbany, Reihaneh, Takaffoli, Mansoreh, Fagnan, Justin, Zaïane, Osmar R., and Campello, Ricardo J.G.B. (2012) Relative validity criteria for community mining algorithms. In: Proceedings of the International Conference on Advances in Social Networks Analysis and Mining, pp. 258-265. From: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 26-29 August 2012, Istanbul, Turkey.

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Abstract

Grouping data points is one of the fundamental tasks in data mining, which is commonly known as clustering if data points are described by attributes. When dealing with interrelated data that does not have any attributes and is represented in the form of nodes and their relationships, this task is also referred to as community mining. There has been a considerable number of approaches proposed in recent years for mining communities in a given network. But little work has been done on how to evaluate community mining results. The common practice is to use an agreement measure to compare the mining result against a ground truth, however, the ground truth is not known in most of the real world applications. In this paper, we investigate relative clustering quality measures defined for evaluation of clustering data points with attributes and propose proper adaptations to make them applicable in the context of social networks. Not only these relative criteria could be used as metrics for evaluating quality of the groupings but also they could be used as objectives for designing new community mining algorithms.

Item ID: 47061
Item Type: Conference Item (Research - E1)
ISBN: 978-1-4673-2497-7
Funders: FAPESP, CNPq
Date Deposited: 04 Jan 2017 08:04
FoR Codes: 01 MATHEMATICAL SCIENCES > 0104 Statistics > 010401 Applied Statistics @ 100%
SEO Codes: 97 EXPANDING KNOWLEDGE > 970101 Expanding Knowledge in the Mathematical Sciences @ 100%
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