Improving Similarity Join Algorithms Using Fuzzy Clustering Technique

I
ICDMW
2009
2009 IEEE International Conference on Data Mining Workshops
Abstract - Improving Similarity Join Algorithms Using Fuzzy Clustering Technique

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2009 IEEE International Conference on Data Mining Workshops

Miami, Florida, USA

December 06-December 06

ISBN: 978-0-7695-3902-7

	ASCII Text	x
	Lisa Tan, Farshad Fotouhi, William Grosky, Horia F. Pop, Noureddine Mouaddib, "Improving Similarity Join Algorithms Using Fuzzy Clustering Technique," Data Mining Workshops, International Conference on, pp. 545-550, 2009 IEEE International Conference on Data Mining Workshops, 2009.

	BibTex	x
	@article{ 10.1109/ICDMW.2009.50, author = {Lisa Tan and Farshad Fotouhi and William Grosky and Horia F. Pop and Noureddine Mouaddib}, title = {Improving Similarity Join Algorithms Using Fuzzy Clustering Technique}, journal ={Data Mining Workshops, International Conference on}, volume = {0}, year = {2009}, isbn = {978-0-7695-3902-7}, pages = {545-550}, doi = {http://doi.ieeecomputersociety.org/10.1109/ICDMW.2009.50}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Data Mining Workshops, International Conference on TI - Improving Similarity Join Algorithms Using Fuzzy Clustering Technique SN - 978-0-7695-3902-7 SP545 EP550 A1 - Lisa Tan, A1 - Farshad Fotouhi, A1 - William Grosky, A1 - Horia F. Pop, A1 - Noureddine Mouaddib, PY - 2009 VL - 0 JA - Data Mining Workshops, International Conference on ER -

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDMW.2009.50

In this paper, we propose a pre-processing technique to improve existing string similarity join algorithms using fuzzy clustering. Our approach first identifies groups of related attributes and then, using this information, we apply existing string similarity join algorithms on these attributes. To identify the clustered attributes we use fuzzy techniques. This approach can be applied to the integration of knowledge bases and databases, as well as handle inconsistent values and naming conventions, incorrect or missing data values, and incomplete information from multiple sources with semi-compatible attributes or homogenous attributes. Using an experimental study, we have shown our preprocessing approach improves existing string similarity join algorithms by about 10 percent on precision and recall.

Citation:

Lisa Tan, Farshad Fotouhi, William Grosky, Horia F. Pop, Noureddine Mouaddib, "Improving Similarity Join Algorithms Using Fuzzy Clustering Technique," icdmw, pp.545-550, 2009 IEEE International Conference on Data Mining Workshops, 2009

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