Abstract
Cluster ensembles can combine the outcomes of several clusterings to a single clustering that agrees as much as possible with the input clusterings. However, little attention has been paid to the development of approaches to deal with consolidating the outcomes of both soft and hard clustering systems into a single final partition. For this reason, this paper proposes a cluster ensemble framework based on three-way decisions, and the interval sets used here to represent the cluster which is described by three regions according to the lower and upper bound of the cluster. In addition, this paper also devises a plurality voting-based consensus function which can consolidate the outcomes of multiple clustering systems whatever the systems are soft clustering systems or hard clustering systems. The proposed consensus function has been evaluated both in the quality of consensus partitions and in the running time.
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Yu, H., Zhou, Q. (2013). A Cluster Ensemble Framework Based on Three-Way Decisions. In: Lingras, P., Wolski, M., Cornelis, C., Mitra, S., Wasilewski, P. (eds) Rough Sets and Knowledge Technology. RSKT 2013. Lecture Notes in Computer Science(), vol 8171. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41299-8_29
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DOI: https://doi.org/10.1007/978-3-642-41299-8_29
Publisher Name: Springer, Berlin, Heidelberg
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