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Testing Various Similarity Metrics and their Permutations with Clustering Approach in Context Free Data Cleaning
Sohil Dineshkumar Pandya, Paresh V Virparia
Pages - 344 - 350     |    Revised - 30-10-2009     |    Published - 30-11-2009
Volume - 3   Issue - 5    |    Publication Date - November 2009  Table of Contents
Context free data cleaning, Clustering, Sequence similarity metrics
Organizations can sustain growth in this knowledge era by proficient data analysis, which heavily relies on quality of data. This paper emphasizes on usage of sequence similarity metric with clustering approach in context free data cleaning to improve the quality of data by reducing noise. Authors propose an algorithm to test suitability of value to correct other values of attribute based on distance between them. The sequence similarity metrics like Needlemen-Wunch, Jaro-Winkler, Chapman Ordered Name Similarity and Smith-Waterman are used to find distance of two values. Experimental results show that how the approach can effectively clean the data without reference data.
CITED BY (3)  
1 Agarwal, M. (2013). Text steganographic approaches: a comparison. arXiv preprint arXiv:1302.2718.
2 S. D. Pandya and P. V. Virparia, “Context Free Data Cleaning and its Application in Mechanism for Suggestive Data Cleaning”, International Journal of Information Science, 1(1), pp. 32-35, 2011.
3 R. Ahmad and A. Khanum, “Document Topic Generation in Text Mining by using Cluster Analysis with EROCK”, International Journal of Computer Science and Security (IJCSS), 4(2), pp. 176 – 182, 2010.
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Mr. Sohil Dineshkumar Pandya
Dr. Paresh V Virparia
G H Patel PG Dept of Computer Science & Technology - India