A Novel Clustering Method for Similarity Measuring in Text Documents
Journal Title: International Journal of Modern Engineering Research (IJMER) - Year 2013, Vol 3, Issue 5
Abstract
Clustering is the process of grouping data into subsets in such a manner that identical instances are collected together, while different instances belong to different groups. The instances are thereby arranged into an efficient depiction that characterizes the populace that is being sampled. A general move towards the clustering process is to treat it as an optimization process. A best partition is found by optimizing an exacting function of similarity, or distance, among data. Basically, there is a hidden assumption that the true inherent structure of data could be correctly describe by using the similarity formula defined and fixed in the clustering decisive factor. In this paper, we introduce clustering with multi- view points based on different similarity measures. The multi- view point approach to learning is one in which we have ‘views’ of the data (sometimes in a rather abstract sense) and the goal is to use the relationship between these views to alleviate the difficulty of a learning problem of interest.
Authors and Affiliations
Preethi Priyanka Thella
Keywords
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- EP ID EP87814
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