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Abstract
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Article Information:
A New Optimized Data Clustering Technique using Cellular Automata and Adaptive Central Force Optimization (ACFO)
G. Srinivasa Rao, Vakulabharanam Vijaya Kumar and Penmesta Suresh Varma
Corresponding Author: G. Srinivasa Rao
Submitted: September 27, 2014
Accepted: February 22, 2015
Published: June 15, 2015 |
Abstract:
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As clustering techniques are gaining more important today, we propose a new clustering technique by means of ACFO and cellular automata. The cellular automata uniquely characterizes the condition of a cell at a specific moment by employing the data like the conditions of a reference cell together with its adjoining cell, total number of cells, restraint, transition function and neighbourhood calculation. With an eye on explaining the condition of the cell, morphological functions are executed on the image. In accordance with the four stages of the morphological process, the rural and the urban areas are grouped separately. In order to steer clear of the stochastic turbulences, the threshold is optimized by means of the ACFO. The test outcomes obtained vouchsafe superb performance of the innovative technique. The accomplishment of the new-fangled technique is assessed by using additional number of images and is contrasted with the traditional methods like CFO (Central Force Optimization) and PSO (Particle Swarm Optimization).
Key words: ACFO (Adaptive Central Force Optimization), cellular automata, convolution, correlation, morphological operation, ,
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Cite this Reference:
G. Srinivasa Rao, Vakulabharanam Vijaya Kumar and Penmesta Suresh Varma, . A New Optimized Data Clustering Technique using Cellular Automata and Adaptive Central Force Optimization (ACFO). Research Journal of Applied Sciences, Engineering and Technology, (5): 522-531.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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