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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Vydáno v:2013 International Conference on Recent Trends in Information Technology (ICRTIT) s. 546 - 550
Hlavní autoři: Rani, A. Jaya Mabel, Parthipan, Latha
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.07.2013
Témata:
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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Shrnutí: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