Simultaneous Pattern and Data Clustering Using Modified K-Means Algorithm
Journal Title: International Journal on Computer Science and Engineering - Year 2010, Vol 2, Issue 6
Abstract
In data mining and knowledge discovery, for finding the ignificant 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 roblems in Pattern Discovery, a new method that imultaneously clusters the discovered patterns and their ssociated data. It is referred to as “Simultaneous pattern and data clustering using Modified K-means lgorithm”. One mportant property of the proposed method is that each attern cluster is explicitly associated with a corresponding ata cluster. Modified means algorithm is used to cluster patterns nd their associated data. After clusters are found, each of them can be further explored and analyzed individually. The roposed method reduces the number of iterations to cluster the given data. The xperimental results using the proposed algorithm with a group of randomly constructed data sets are very promising.
Authors and Affiliations
M. Pramod Kumar , Prof K V Krishna Kishore
Keywords
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