A weight-incorporated similarity-based clustering ensemble method based on swarm intelligence

Document Type

Article

Publication Date

7-15-2016

Abstract

Clustering methods play an important role in data mining and various other applications. This work investigates them based on swarm intelligence. It proposes a new clustering method by combining K-means clustering method and mussels wandering optimization algorithm. A single cluster method is well recognized to achieve limited performance when it is compared with a clustering ensemble (CE) that integrates several single ones. Hence, this work introduces a new CE method called weight-incorporated similarity- based CE. The commonly-used datasets with varying size are used to test the performance of the proposed methods. The simulation results illustrate the validity and performance advantages of the proposed ones over some of their peers.

Identifier

84969705896 (Scopus)

Publication Title

Knowledge Based Systems

External Full Text Location

https://doi.org/10.1016/j.knosys.2016.04.021

ISSN

09507051

First Page

156

Last Page

164

Volume

104

Grant

1162482

Fund Ref

National Science Foundation

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