Abstract
This paper presents a simple method for mining both positive and negative association rules in databases using singular value decomposition (SVD) and similarity measures. In literature, SVD is used for summarizing matrices. We use transaction-item price matrix to generate so called ratio rules in the literature. Transaction-item price matrix is formed by using the price data of corresponding items from the sales transactions. Ratio rules are generated by running SVD on transaction-item price matrix. We then use similarity measures on a subset of rules found by Pareto analysis to determine positive and negative associations. The proposed method can present the positive and negative associations with their strengths. We obtain subsequent results using cosine and correlation similarity measures.
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