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Abstract

Discovering underlying structure from co-occurrence data is an important task in a variety of fields, including: insurance, intelligence, criminal investigation, epidemiology, human resources, and marketing. Previously Kubica et. al. presented the group detection algorithm (GDA) - an algorithm for finding underlying groupings of entities from co-occurrence data. This algorithm is based on a probabilistic generative model and produces coherent groups that are consistent with prior knowledge. Unfortunately, the optimization used in GDA is slow, potentially making it infeasible for many large data sets. To this end, we present k-groups - an algorithm that uses an approach similar to that of k-means to significantly accelerate the discovery of groups while retaining GDA's probabilistic model. We compare the performance of GDA and k-groups on a variety of data, showing that k-groups' sacrifice in solution quality is significantly offset by its increase in speed.

Keywords

Data Mining, Link Analysis

Notes

Number of pages: 4

Text Reference

Jeremy Martin Kubica, Andrew Moore, and Jeff Schneider, "Tractable Group Detection on Large Link Data Sets," The Third IEEE International Conference on Data Mining, November, 2003, pp. 573-576.

BibTeX Reference

@inproceedings{Kubica_2003_4548,
   author = "Jeremy Martin Kubica and Andrew Moore and Jeff Schneider",
   editor = "Xindong Wu and Alex Tuzhilin and Jude Shavlik",
   title = "Tractable Group Detection on Large Link Data Sets",
   booktitle = "The Third IEEE International Conference on Data Mining",
   pages = "573-576",
   publisher = "IEEE Computer Society",
   month = "November",
   year = "2003",
}

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