ABSTRACT

In data mining and knowledge discovery, for finding the significant correlation among events Pattern discovery (PD) is used. PD typically produces an overwhelming number of patterns. Since there are too many patterns, it is difficult to use them to further explore or analyze the data. To address the problems in Pattern Discovery, a new method that simultaneously clusters the discovered patterns and their associated data. It is referred to as “Simultaneous pattern and data clustering using Modified K-means Algorithm”. One important property of the proposed method is that each pattern cluster is explicitly associated with a corresponding data cluster. Modified Kmeans algorithm is used to cluster patterns and their associated data. After clusters are found, each of them can be further explored and analyzed individually. The proposed method reduces the number of iterations to cluster the given data. The experimental results using the proposed algorithm with a group of randomly constructed data sets are very promising.