2008 EffectiveandEfficientItemsetPat

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In this paper, we propose a set of novel regression-based approaches to effectively and efficiently summarize frequent itemset patterns. Specifically, we show that the problem of minimizing the restoration error for a set of itemsets based on a probabilistic model corresponds to a non-linear regression problem. We show that under certain conditions, we can transform the nonlinear regression problem to a linear regression problem. We propose two new methods, k-regression and tree-regression, to partition the entire collection of frequent itemsets in order to minimize the restoration error. The K-regression approach, employing a K-means type clustering method, guarantees that the total restoration error achieves a local minimum. The tree-regression approach employs a decision-tree type of top-down partition process. In addition, we discuss alternatives to estimate the frequency for the collection of itemsets being covered by the k representative itemsets. The experimental evaluation on both real and synthetic datasets demonstrates that our approaches significantly improve the summarization performance in terms of both accuracy (restoration error), and computational cost.

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2008 EffectiveandEfficientItemsetPat	Ruoming Jin Yang Xiang Muad Abu-Ata Ning Ruan			Effective and Efficient Itemset Pattern Summarization: Regression-based Approaches		KDD-2008 Proceedings		10.1145/1401890.1401941		2008