Clustering by Hybrid K-Harmonic Means and Biogeography Based Optimization Algorithm for Medical Data
A. Jaya Mabel Rani1, A. Pravin2

1A. Jaya Mabel Rani*, Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai, India.
2Dr. A. Pravin, Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai, India.
Manuscript received on March 15, 2020. | Revised Manuscript received on March 27, 2020. | Manuscript published on April 10, 2020. | PP: 517-522 | Volume-9 Issue-6, April 2020. | Retrieval Number: E3207039520/2020©BEIESP | DOI: 10.35940/ijitee.E3207.049620
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Abstract: In today trendy world hybrid based optimized data clustering is unique and imperative clustering tool in the area of data mining, which is dynamic research of actual creation problems. The oldest and furthermost commonly used popular clustering technique is the K-means(KM) algorithm, which is very complex and for the initialization of the cluster centroid and it will easily go for premature converge. This initialization problem of K-means can be evaded by built in boost function of K-Harmonic Means, which is centroid based clustering algorithm and also unresponsive for collection of initial partition clustering , but it can easily go for pre-matured conjunction in local optima. To avoid this convergence problem, this proposed algorithm uses Boosting K-harmonic means(KHM) algorithm with BBO to produce more precise, robust, better clustering solution in few number of iterations, evade conning in local optima and simply convergence to relate with Harmonic Means, BBO algorithms. Biogeography based algorithm works with the concept of emigration and immigration of inhabitants from one location to another location, Which has high computation cost. For avoiding this high computation cost in this hybrid optimization technique Biogeography-Based Optimization (BBO) is integrated with K-Harmonic means algorithm to produce optimum and effective clustering solution with faster convergence. BBO is universal optimization methods to solve utmost of the optimization problem, which is an production based generation of evolutionary algorithm (EA)that augments a function by stochastically and re peatedly improving the clustering solution of quality, or fitness function. The experimental results of this paper shown as the projected method is very resourceful and faster to afford better clustering solution in less number of repetitions for medical data. 
Keywords: BBO, Clustering, Data mining, K-means, Optimized intelligent tool.
Scope of the Article: Clustering