Abstract
Clustering or cluster analysis is an important and common task in data mining and analysis, with applications in many fields. However, most existing clustering methods are sensitive in the presence of limited amounts of data per cluster in real-world applications. Here we propose a new method called denoising cluster analysis to improve the accuracy. We first construct base clusterings with artificially corrupted data samples and later learn their ensemble based on mutual information. We develop multiplicative updates for learning the aggregated cluster assignment probabilities. Experiments on real-world data sets show that our method unequivocally improves cluster purity over several other clustering approaches.
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