DMCA
Similarity Testing Between Heterogeneous Basket Datasets (2002)
BibTeX
@TECHREPORT{Vers02similaritytesting,
author = {Un I Vers and I Ty Of and Tao Li and Tao Li and Mitsunori Ogihara and Mitsunori Ogihara},
title = {Similarity Testing Between Heterogeneous Basket Datasets},
institution = {},
year = {2002}
}
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Abstract
This paper proposes a new measure for similarity between basket datasets. The new measure is calculated from support counts using a formula inspired by information entropy. Experiments on both real and synthetic datasets show the effectiveness of the measure. This paper also studies the problem of finding a mapping between categorical database attribute sets using similarity measures. A generic approach for identifying such a mapping is proposed. The approach is implemented based on the similarity measure proposed in the paper and its performance has been evaluated and validated. Moreover, this paper also explores the applications of using the similarity measure to mine distributed datasets.
