A data clustering algorithm based on mussels wandering optimization

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 fi...

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Bibliographic Details
Published inProceedings of the 11th IEEE International Conference on Networking, Sensing and Control pp. 713 - 718
Main Authors Peng Yan, ShiYao Liu, Qi Kang, BingYao Huang, MengChu Zhou
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2014
Subjects
clustering
data mining
Iris
mussels wandering optimization
optimization
Particle swarm optimization
Reactive power
Sociology
Statistics
swarm intelligence
Vehicles
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DOI10.1109/ICNSC.2014.6819713

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Summary: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.
DOI:10.1109/ICNSC.2014.6819713