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 ...

• Type: Conference
• Year: 2008
• Where: KDD
• Authors: Ruoming Jin, Muad Abu-Ata, Yang Xiang, Ning Ruan
• Keywords: Data Mining, KDD 2008, Non-linear Regression Problem, Restoration Error, Total Restoration Error