FuzzifiedPSO and K-Harmonic means algorithm for electrical data clustering

This paper proposed Fuzzified Particle Swarm Optimization and K-Harmonic Means algorithm (FPSO+KHM) for clustering the Electrical data systems. Thepartitioned clustering algorithms are more suitable for clustering large datasets. The K-Harmonic means algorithm is center based clustering algorithm an...

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Veröffentlicht in:	2013 International Conference on Recent Trends in Information Technology (ICRTIT) S. 546 - 550
Hauptverfasser:	Rani, A. Jaya Mabel, Parthipan, Latha
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 01.07.2013
Schlagworte:	Centroid Clustering Clustering algorithms Convergence Equations Fuzzified PSO Harmonic analysis K-Harmonic means algorithm local optima Mathematical model Particle swarm optimization Partitioning algorithms
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Zusammenfassung:	This paper proposed Fuzzified Particle Swarm Optimization and K-Harmonic Means algorithm (FPSO+KHM) for clustering the Electrical data systems. Thepartitioned clustering algorithms are more suitable for clustering large datasets. The K-Harmonic means algorithm is center based clustering algorithm and veryinsensitive to the selection of initial partition usingbuilt in boost function, but easily convergence in local optima. The proposed algorithm uses Fuzzified PSO and K-harmonic means algorithm to generate more accurate, robust, better clustering results, best solution in few number of iterations, avoid trapping in local optima and get faster convergence when compare to K-Harmonic Meansand hybrid PSO+ K-Harmonic Means algorithms. This algorithm is applied for two different set of IEEE bus electrical data systems.
DOI:	10.1109/ICRTIT.2013.6844261