Conference Proceedings

Generation of alternative clusterings using the CAMI approach

XH Dang, J Bailey

Proceedings of the 10th SIAM International Conference on Data Mining, SDM 2010 | Published : 2010


Exploratory data analysis aims to discover and generate multiple views of the structure within a dataset. Conventional clustering techniques, however, are designed to only provide a single grouping or clustering of a dataset. In this paper, we introduce a novel algorithm called CAMI, that can uncover alternative clusterings from a dataset. CAMI takes a mathematically appealing approach, combining the use of mutual information to distinguish between alternative clusterings, coupled with an expectation maximization framework to ensure clustering quality. We experimentally test CAMI on both synthetic and real-world datasets, comparing it against a variety of state-of-the-art algorithms. We demo..

View full abstract

Citation metrics