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Ensemble Clustering Data Mining and Databases

Ensemble Clustering Data Mining and Databases

Slawomir T. Wierzchon
Copyright: © 2018 |Pages: 12
ISBN13: 9781522522553|ISBN10: 1522522557|EISBN13: 9781522522560
DOI: 10.4018/978-1-5225-2255-3.ch170
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MLA

Wierzchon, Slawomir T. "Ensemble Clustering Data Mining and Databases." Encyclopedia of Information Science and Technology, Fourth Edition, edited by Mehdi Khosrow-Pour, D.B.A., IGI Global, 2018, pp. 1962-1973. https://doi.org/10.4018/978-1-5225-2255-3.ch170

APA

Wierzchon, S. T. (2018). Ensemble Clustering Data Mining and Databases. In M. Khosrow-Pour, D.B.A. (Ed.), Encyclopedia of Information Science and Technology, Fourth Edition (pp. 1962-1973). IGI Global. https://doi.org/10.4018/978-1-5225-2255-3.ch170

Chicago

Wierzchon, Slawomir T. "Ensemble Clustering Data Mining and Databases." In Encyclopedia of Information Science and Technology, Fourth Edition, edited by Mehdi Khosrow-Pour, D.B.A., 1962-1973. Hershey, PA: IGI Global, 2018. https://doi.org/10.4018/978-1-5225-2255-3.ch170

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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 do this task. In this article we clarify the idea of consensus clustering, and we 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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