document: 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 .
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