Abstract
Short-texts mining has become an important area of research in IR and data mining. Ncut-term weighting is recently proposed for clustering of short-texts using non-negative matrix factorization. Non-negative factorization can be employed for such term weighting when the similarity measure is the inner product of term-document matrix. We propose a new weighting scheme and devise a new clustering algorithm using Hadamard product of similarity matrices. We demonstrate that our technique yields much better clustering in comparison to ncut weighting scheme. We use three measures for evaluating clustering qualities, namely purity, normalized mutual information and adjusted Rand index. We use standard benchmark datasets and also compare the performance of our algorithm with well-known document clustering technique of Ng-Jordan-Weiss. Experimental results suggest that the weighting process by Hadamard product gives better clustering of document of short-texts.
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http://archive.ics.uci.edu/ml/datasets/Pen-Based+Recognition+of+Handwritten+Digits
http://archive.ics.uci.edu/ml/datasets/Iris
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Verma, K., Jadon, M.K., Pujari, A.K. (2013). Clustering Short-Text Using Non-negative Matrix Factorization of Hadamard Product of Similarities. In: Banchs, R.E., Silvestri, F., Liu, TY., Zhang, M., Gao, S., Lang, J. (eds) Information Retrieval Technology. AIRS 2013. Lecture Notes in Computer Science, vol 8281. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45068-6_13
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DOI: https://doi.org/10.1007/978-3-642-45068-6_13
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