document: 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 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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