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

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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.

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Funding was provided by National Natural Science Foundation of China (Grant No. 71501186).

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Correspondence to Hong Yin.

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Feng, Y., Lu, H., Xie, W. et al. An Improved Fuzzy C-Means Clustering Algorithm Based on Multi-chain Quantum Bee Colony Optimization. Wireless Pers Commun 102, 1421–1441 (2018). https://doi.org/10.1007/s11277-017-5203-2

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  • Fuzzy c-means
  • Artificial bee colony algorithm
  • Expansion of multi-chain coding
  • Gene chain
  • Quantum bee colony algorithm