Document Clustering using Feature Selection Based on Multiviewpoint and Link Similarity Measure
نویسندگان
چکیده
Clustering is one of the very powerful and widely used technique in information retrieval. All clustering methods works on finding relationship among data objects. There are various similarity measures used along with criterion functions to find similarity between documents like cosine, jaccard etc. Clustering efficiency and performance is highly dependent on the accuracy of the similarity measure. In this paper we propose a new similarity measure based on link and multiviewpoint similarity to find the closeness of two documents.
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