Abstract
This paper presents an efficient hybrid method, namely fuzzy particle swarm optimization (MFPSO) to solve the fuzzy clustering problem, especially for large sizes. When the problem becomes large, the FCM algorithm may result in uneven distribution of data, making it difficult to find an optimal solution in reasonable amount of time. The PSO algorithm does find a good or near-optimal solution in reasonable time. In our work it is shown that its performance may be improved by seeding the initial swarm with the result of the c-means algorithm. Various clustering simulations are experimentally compared with the FCM algorithm in order to illustrate the efficiency and ability of the proposed algorithms.
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Satapathy, S.C., Patnaik, S.K., Dash, C.D.P., Sahoo, S. (2011). Data Clustering Using Modified Fuzzy-PSO (MFPSO). In: Sombattheera, C., Agarwal, A., Udgata, S.K., Lavangnananda, K. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2011. Lecture Notes in Computer Science(), vol 7080. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25725-4_12
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DOI: https://doi.org/10.1007/978-3-642-25725-4_12
Publisher Name: Springer, Berlin, Heidelberg
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