ttp://iaeme.com/Home/journal/IJCET 320 editor@iaeme.com h International Journal of Computer Engineering and Technology (IJC ) ET Volume 9, Issue 05, September - October 2018, pp. 320-325, Article ID: IJC _09_05_035 ET Available online at ttp://iaeme.com/Home/issue/IJCET?Volume=9&Issue=5 h Journal Impact Factor (2018): 9.9120 (Calculated by GISI) www.jifactor.com ISSN Print: 0976-6367 and ISSN Online: 0976 6375 – © IAEME Publication HYBRID OPTIMIZED ALGORITHMS FOR SOLVING CLUSTERING PROBLEMS IN DATA MINING S. Karthikeyan Department of Computer Science, Rathinam College of Arts and Science, Coimbatore, Tamilnadu, India ABSTRACT In this paper, Cluster analysis is a group objects like observations, events etc based on the information that are found in the data describing the objects or their relations. The main goal of the clustering is that the objects in a group will be similar or related to one other and different from (or unrelated to) the objects in other groups. Extracting relevant information from large database is attaining huge significance. Clustering of relevant information from large database becomes difficult. The major objective of this work is to proposed novel clustering methods for solving clustering problem. Data Mining is too possible to chunk away, concealed helpful acquaintance and data from profuse, imperfect, noisy, fuzzy and random realistic data. In data mining, the clustering method is one of the popular methods to be used. It is used to separate the data set into a significant set of reciprocally limited clusters with respect to relationship of data and it is used to create the more number of data in the same manner surrounded by a group and extra various among groups. Data clustering is a vital concept of mining as it partitions the given dataset into meaningful set of clusters based on data similarity. This concept enhances the computation efficiency in the data analysis processes. Key words: Clustering, ABC Algorithm, PSO and FA Algorithm, MOSSSA- , HAC MOSSCS-MHAC Algorithms. Cite this Article: S. Karthikeyan, Hybrid Optimized Algorithms for Solving Clustering Problems in Data Mining, International Journal of Computer Engineering and Technology 9(5), 2018, pp. 320-325. ttp://iaeme.com/Home/issue/IJCET?Volume=9&Issue=5 h 1. INTRODUCTION Clustering is an information mining strategy to aggregate the comparative information into a cluster and disparate information into various groups. The objective of clustering is to gather information into groups to such an extent that the similitude between information individuals inside a similar cluster are maximal while likenesses between the information individuals from various clusters are negligible. The optimization performance is not achieved and hence the overall clustering performance is reduced significantly. To overcome the above mentioned issues, the optimization based clustering algorithms are proposed. The optimization algorithms