A Modified Hierarchical Clustering Algorithm for Document Clustering
Journal Title: International Journal of Advanced Research in Computer Engineering & Technology(IJARCET) - Year 2013, Vol 2, Issue 6
Abstract
Clustering is the division of data into groups called as clusters. Document clustering is done to analyse the large number of documents distributed over various sites. The similar documents are grouped together to form a cluster. The success or failure of a clustering method depends on the nature of similarity measure used. The multiviewpoint-based similarity measure or MVS uses different viewpoints unlike the traditional similarity measures that use only a single viewpoint. This increases the accuracy of clustering. A hierarchical clustering algorithm creates a hierarchical tree of the given set of data objects. Depending on the decomposition approach, hierarchical algorithms are classified as agglomerative (merging) or divisive (splitting). This paper focuses on applying multiviewpoint-based similarity measure on hierarchical clustering
Authors and Affiliations
Merin Paul , P Thangam
Keywords
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