Applications of MVS on Hierarchical Clustering Algorithms
Journal Title: International Journal of Research in Computer and Communication Technology - Year 2013, Vol 2, Issue 12
Abstract
Clustering is a valuable method that arranges a large quantity of unordered text documents into a small number of expressive and coherent clusters, thereby offering a basis for instinctive and informative steering and browsing mechanisms. There are some clustering methods which have to adopt some cluster relationship among the data objects that they are applied on. Similarity between a pair of objects can be defined either explicitly or implicitly. The main difference between a traditional dissimilarity/similarity measure and ours is that the former uses only a only a single lookout, which is the origin, while the latter utilizes many diverse viewpoints, which are objects assumed to not be in the same cluster with the two objects being measured. Using multiple viewpoints, more informative assessment of similarity could be achieved. Theoretical analysis and experimental study are conducted to support this claim. Two criterion functions for document Hierarchical clustering are proposed and shown that the proposed method has significant improvement as compare to the existing methods.
Authors and Affiliations
Ch. SKVR Naidu, V Ramu, D Srinivas
Keywords
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