Improve the Efficiency of Image Segmentation Scheme using Swarm Intelligence Techniques
Akanksha Garg1, Shiv K. Sahu2
1Akanksha Garg, M.Tech Scholar, Department of Computer Science & Engineering, Lakshmi Narain College of Technology Excellence, Bhopal (Madhya Pradesh), India.
2Dr. Shiv K. Sahu, Professor & Head, Department of Computer Science & Engineering, Lakshmi Narain College of Technology Excellence, Bhopal (Madhya Pradesh), India.
Manuscript received on 10 June 2017 | Revised Manuscript received on 20 June 2017 | Manuscript Published on 30 June 2017 | PP: 4-7 | Volume-6 Issue-10, June 2017 | Retrieval Number: J24340661017/17©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Clustering analysis is a primitive exploratory approach in data analysis with little or no prior knowledge. Clustering has been widely used for data analysis and been an active subject in several research fields such as pattern recognition, information retrieval, data mining applications, bioinformatics and many others. This paper presents a particle of swarm optimization with self-optimal clustering (SOC) technique which is an advanced version of improved mountain clustering (IMC) technique. Proposed POS based SOC clustering techniques for large data. We used the POS for the selection of important parameter such as value of centroid and center, this parameter decides the selection of center point of cluster technique. The SOC clustering technique decides the cluster level wise seed and generates cluster according to their features attribute of data. The experiments also revealed the convergence property of the level fitness in Proposed. We compared our Proposed with existing clustering algorithms and shows that the results are improved.
Keywords: Improved Mountain Clustering, Elf Optimal Clustering, Particle Swarm Optimization, K-means, CRM.
Scope of the Article: Image Security