Improved Fuzzy Clustering Algorithm Based on Intelligent Computing

FCM algorithm is easy to be affected by fuzzy parameters, initial clustering, noise, and because of the single iteration path, the phenomenon of local extremum can be generated. Although it was improvd by artificial bee colony algorithm, artificial bee colony has some shortcomings, such as the prema...

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Bibliographic Details
Published in:	2017 International Conference on Robots & Intelligent System (ICRIS) pp. 161 - 164
Main Author:	Zhu Tianyuan
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01.10.2017
Subjects:	artificial bee colony clustering center fuzzy c-means Intelligent systems Robots Xenon
Online Access:	Get full text
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Summary:	FCM algorithm is easy to be affected by fuzzy parameters, initial clustering, noise, and because of the single iteration path, the phenomenon of local extremum can be generated. Although it was improvd by artificial bee colony algorithm, artificial bee colony has some shortcomings, such as the premature convergence, low precision and slow convergence. A kind of improved artificial swarm algorithm based on improved search strategy is proposed and is also used in fuzzy c-means algorithm. The experiments based on UCI datasets show that this new algorithm overcomes the disadvanhges of FCM. Besides, it has higher clustering accuracy rate.
DOI:	10.1109/ICRIS.2017.47