document: Summarizing itemset patterns : a profile-based approach Frequent-pattern mining has been studied extensively on scalable methods for mining various kinds of patterns including itemsets , sequences , and graphs . However , the bottleneck of frequent-pattern mining is not at the efficiency but at the interpretability , due to the huge number of patterns generated by the mining process . In this paper , we examine how to summarize a collection of itemset patterns using only K representatives , a small number of patterns that a user can handle easily . The K representatives should not only cover most of the frequent patterns but also approximate their supports . A generative model is built to extract and profile these representatives , under which the supports of the patterns can be easily recovered without consulting the original dataset . Based on the restoration error , we propose a quality measure function to determine the optimal value of parameter K. Polynomial time algorithms are developed together with several optimization heuristics for efficiency improvement . Empirical studies indicate that we can obtain compact summarization in real datasets .
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