Summarizing itemset patterns using probabilistic models

In this paper, we propose a novel probabilistic approach to summarize frequent itemset patterns. Such techniques are useful for summarization, post-processing, and end-user interpretation, particularly for problems where the resulting set of patterns are huge. In our approach items in the dataset are modeled as random variables. We then construct a Markov Random Fields (MRF) on these variables based on frequent itemsets and their occurrence statistics. The summarization proceeds in a level-wise iterative fashion. Occurrence statistics of itemsets at the lowest level are used to construct an initial MRF. Statistics of itemsets at the next level can then be inferred from the model. We use those patterns whose occurrence can not be accurately inferred from the model to augment the model in an iterative manner, repeating the procedure until all frequent itemsets can be modeled. The resulting MRF model affords a concise and useful representation of the original collection of itemsets. Extensive empirical study on real datasets show that the new approach can effectively summarize a large number of itemsets and typically significantly outperforms extant approaches.