Comparison Clustering using Cosine and Fuzzy set based Similarity Measures of Text Documents
Information Retrieval
2015-05-04 v1
Abstract
Keeping in consideration the high demand for clustering, this paper focuses on understanding and implementing K-means clustering using two different similarity measures. We have tried to cluster the documents using two different measures rather than clustering it with Euclidean distance. Also a comparison is drawn based on accuracy of clustering between fuzzy and cosine similarity measure. The start time and end time parameters for formation of clusters are used in deciding optimum similarity measure.
Keywords
Cite
@article{arxiv.1505.00168,
title = {Comparison Clustering using Cosine and Fuzzy set based Similarity Measures of Text Documents},
author = {Manan Mohan Goyal and Neha Agrawal and Manoj Kumar Sarma and Nayan Jyoti Kalita},
journal= {arXiv preprint arXiv:1505.00168},
year = {2015}
}
Comments
4 pages, International Conference on Computing and Communication Systems 2015 (I3CS'15), ISBM: 978-1-4799-5857-01, 2015