| Effective and Efficient Itemset Pattern Summarization: Regression-based Approaches (2009) | |||||||||||||||
Abstract | |||||||||||||||
| 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 non-linear 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 treeregression approach employs a decision-tree type of top-down partitionprocess. Inaddition,wediscussalternativestoestimate the frequency for the collection of itemsets being covered by the k representative itemsets. The experimental evaluation on both realandsyntheticdatasetsdemonstratesthatourapproachessignificantly improve the summarization performance in terms of bothaccuracy (restorationerror),andcomputational cost. | |||||||||||||||
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