Abstract
In this paper, a new possibilistic C-means (PCM) clustering algorithm is proposed based on particle swarm optimization (PSO) and simulated annealing (SA). Two optimizing algorithms automatically define the centers and number of clustering by different searching mechanisms in each sub-swarms and collaborative interaction among sub-swarms, which provides the optimal initialization for the new algorithm’s good clustering result within a bid to solve the problem that PCM algorithm is very sensitive to initialization and parameter. The new PCM has the strong ability in the global searching and has less sensitivity to initialization and less possibility of stagnation in local optimum. Furthermore, the PSO-SA defines the centers and numbers of clustering automatically. Its advantages lie in the fact that it can not only improve the clustering performance but also has better accuracy and robustness, avoiding the problem of coincident clusters.
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Acknowledgments
This research is partly supported by Research on Case-based Reasoning in Precision Spade Punch Planter Design Theory and Method (Project No. 51075282) from the National Natural Science Fund, China.
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Zang, J., Li, C. (2014). Possibilistic C-means Algorithm Based on Collaborative Optimization. In: Patnaik, S., Li, X. (eds) Proceedings of International Conference on Computer Science and Information Technology. Advances in Intelligent Systems and Computing, vol 255. Springer, New Delhi. https://doi.org/10.1007/978-81-322-1759-6_67
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DOI: https://doi.org/10.1007/978-81-322-1759-6_67
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-1758-9
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