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Ensemble Clustering Data Mining and Databases
Abstract
Standard clustering algorithms employ fixed assumptions about data structure. For instance, the k-means algorithm is applicable for spherical and linearly separable data clouds. When the data come from multidimensional normal distribution, so-called EM algorithm can be applied. But in practice, the assumptions underlying given set of observations are too complex to fit into a single assumption. We can split these assumptions into manageable hypothesis justifying the use of particular clustering algorithms. Then we must aggregate partial results into a meaningful description of our data. The consensus clustering does this task. In this chapter, the authors clarify the idea of consensus clustering, and they present conceptual frames for such a compound analysis. Next, the basic approaches to implement consensus procedure are given. Finally, some new directions in this field are mentioned.
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