A data clustering algorithm based on mussels wandering optimization
ICNSC(2014)
摘要
As an unsupervised learning method, clustering methods plays an important role in quality data mining and various other applications. This work investigates them based on swarm intelligence, introduces a new intelligence algorithm called mussels wandering optimization (MWO) to the data clustering field, and proposes a new clustering algorithm by combining K-means clustering method and MWO. Tests on six standard data sets are performed. The results demonstrate the validity and superiority of the proposed method over some representative clustering ones.
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关键词
pattern clustering,evolutionary computation,k-means clustering method,mussels wandering optimization,unsupervised learning method,clustering methods,data clustering algorithm,mwo,optimization,data mining,unsupervised learning,swarm intelligence,clustering,particle swarm optimization,sociology,statistics,reactive power,iris
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