HYBRID OPTIMIZED ALGORITHMS FOR SOLVING CLUSTERING PROBLEMS IN DATA MINING

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 dif erent 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 dif icult. 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 ef iciency in the data analysis processes.