Relative Validity Criteria for Community Mining Algorithms

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.