Weighted clustering ensemble: : Towards learning the weights of the base clusterings
Pages 421 - 431
Abstract
Clustering ensemble refers to the problem of obtaining a final clustering of a dataset by combining multiple partitions computed by different clustering algorithms. The clustering ensemble has emerged as a prominent method for improving robustness of unsupervised classification solutions. This problem has been received an increasing attention in recent years but a little attention has been paid to weight the combined clusterings without access the original data. We address in this paper the problem of weighted clustering ensemble problem by defining an unsupervised method to compute the weight of each combined clustering without access the original data. The weight of each base clustering is computed using its quality and the quality of its neighbouring clusterings. The proposed method permits to estimate the right number of clusters of the final clustering before the combining step by exploiting the generated weights.
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Published: 01 January 2017
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