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Wireless Personal Communications
Erschienen in: Wireless Personal Communications 2/2018

28.12.2017

An Improved Fuzzy C-Means Clustering Algorithm Based on Multi-chain Quantum Bee Colony Optimization

verfasst von: Yufang Feng, Houqing Lu, Wenbin Xie, Hong Yin, Jingbo Bai

Erschienen in: Wireless Personal Communications | Ausgabe 2/2018

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Abstract

The fuzzy c-means (FCM) algorithm is the most popular clustering method. Many studies of FCM had been done. However, the FCM algorithm and its studies are usually affected by the selection of initial values and noise data, and can easily fall into local optimal value. To overcome these drawbacks of FCM, this paper proposed the algorithm of FCM based on multi-chain quantum bee colony algorithm (MQBC-FCM). In MQBC-FCM, first, the multiple chains encoding method is introduced to the artificial bee colony algorithm to propose the MQBC algorithm. Then MQBC is used to search for the optimal initial clustering centers. The proposed algorithm is used on artificial data sets and image segmentations, and its performance is contrasted with several algorithms. The experimental results have indicated that the proposed MQBC-FCM has efficiently improved the performance of the clustering algorithm.
Vorheriger Artikel Research on Network Security Situation Assessment and Quantification Method Based on Analytic Hierarchy Process
Nächster Artikel Numerical Simulation of Interior Turbulent Flow Regulation of Wet Submersible Motor

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Metadaten
Titel
An Improved Fuzzy C-Means Clustering Algorithm Based on Multi-chain Quantum Bee Colony Optimization
verfasst von
Yufang Feng
Houqing Lu
Wenbin Xie
Hong Yin
Jingbo Bai
Publikationsdatum
28.12.2017
Verlag
Springer US
Erschienen in
Wireless Personal Communications / Ausgabe 2/2018
Print ISSN: 0929-6212
Elektronische ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-017-5203-2

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