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2008
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Effective and efficient itemset pattern summarization: regression-based approaches

9 years 2 months ago
Effective and efficient itemset pattern summarization: regression-based approaches
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 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 ...
Ruoming Jin, Muad Abu-Ata, Yang Xiang, Ning Ruan
Added 30 Nov 2009
Updated 30 Nov 2009
Type Conference
Year 2008
Where KDD
Authors Ruoming Jin, Muad Abu-Ata, Yang Xiang, Ning Ruan
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