Abstract
Clustering ensembles have emerged as a powerful method for improving both the robustness and the stability of unsupervised classification solutions. However, finding a consensus clustering from multiple partitions is a difficult problem that can be approached from graph-based, combinatorial or statistical perspectives. A consensus scheme via the k-modes algorithm is proposed in this paper. A combined partition is found as a solution to the corresponding categorical data clustering problem using the k-modes algorithm. This study compares the performance of the k-modes consensus algorithm with other fusion approaches for clustering ensembles. Experimental results demonstrate the effectiveness of the proposed method.
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Luo, H., Kong, F., Li, Y. (2006). Combining Multiple Clusterings Via k-Modes Algorithm. In: Li, X., Zaïane, O.R., Li, Z. (eds) Advanced Data Mining and Applications. ADMA 2006. Lecture Notes in Computer Science(), vol 4093. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11811305_34
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DOI: https://doi.org/10.1007/11811305_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37025-3
Online ISBN: 978-3-540-37026-0
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