Data mining an non-trivial extraction of novel, implicit, and actionable knowledge from large data sets is an evolving technology which is a direct result of the increasing use of computer databases in order to store and retrieve information effectively. This paper gives an idea of optimization algorithm by which the efficient result can be fetched. Optimization is a dire need for a huge amount of data processing. So that optimization is a challenging issue in data mining. It seems to be that there are many different approaches has been proposed by authors in order to optimize the results. Partial swam optimization and genetic algorithms are some sort of approach which can be used for optimization.

Data Mining, Clustering, Optimized Algorithm, PSO, GA

[1] Satyasai Jagannath Nanda, Ganapati Panda, Automatic clustering algorithm based on multi-objective Immunized PSO to classify actions of 3D human models, Engineering Applications of Artificial Intelligence, Volume 26, Issues 5–6, May–June 2013, Pages 1429-1441
[2] Tuğrul Çavdar, PSO tuned ANFIS equalizer based on fuzzy C-means clustering algorithm, AEU - International Journal of Electronics and Communications, Volume 70, Issue 6, June 2016, Pages 799-807,
[3] Amin Khatami, Saeed Mirghasemi, Abbas Khosravi, Chee Peng Lim, Saeid Nahavandi, A new PSO-based approach to fire flame detection using K-Medoids clustering, Expert Systems with Applications, Volume 68, February 2017, Pages 69-80
[4] Hui-Liang Ling, Jian-Sheng Wu, Yi Zhou, Wei-Shi Zheng, How many clusters? A robust PSO-based local density model, Neurocomputing, Volume 207, 26 September 2016, Pages 264-275
[5] Chen Jinyin, Lin Xiang, Zheng Haibing, Bao Xintong, A novel cluster center fast determination clustering algorithm, Applied Soft Computing, Volume 57, August 2017, Pages 539-55
[6] Xiaojun Chen, Xiaofei Xu, Joshua Zhexue Huang, and Yunming Ye “TW-k-Means: Automated Two-Level Variable Weighting Clustering Algorithm for Multi-view Data” in IEEE Transactions On Knowledge And Data Engineering, Vol. 25, No. 4, April 2013
[7] Enmei Tu , Longbing Cao , Jie Yang , Nicola Kasabov “A novel graph-based k-means for nonlinear manifold clustering and representative selection” in Elsevier transaction of Neuro computing 143 (2014) 109–122
[8] T. Velmurugan “Performance based analysis between k-Means and Fuzzy C-Means clustering algorithms for connection oriented telecommunication data” in Elsevier transaction of Applied Soft Computing 19 (2014) 134–146
[9] Grigorios Tzortzis “The Min Max k-Means clustering algorithm” in Elsevier transaction of Pattern Recognition 47 (2014) 2505–2516
[10] Cheng-Huang Hung , Hua-Min Chiou b, Wei-Ning Yang “Candidate groups search for K-harmonic means data clustering” in Elsevier transaction of Applied Mathematical Modelling 37 (2013) 10123–10128
[11] Yi Ding, Xian Fu, Kernel-based fuzzy c-means clustering algorithm based on genetic algorithm, Neuro Computing, Volume 188, 5 May 2016, Pages 233-238
[12] A. Mukhopadhyay, U. Maulik and S. Bandyopadhyay, "Multiobjective Genetic Algorithm-Based Fuzzy Clustering of Categorical Attributes," in IEEE Transactions on Evolutionary Computation, vol. 13, no. 5, pp. 991-1005, Oct. 2009.
[13] J. Zhang, H. S. H. Chung and W. L. Lo, "Clustering-Based Adaptive Crossover and Mutation Probabilities for Genetic Algorithms," in IEEE Transactions on Evolutionary Computation, vol. 11, no. 3, pp. 326-335, June 2007.