This page accompanies the paper entitled "Tell me what I need to know: succinctly summarizing data with itemsets" The source code of the MTV algorithm can be downloaded here:
Abstract. Data analysis is an inherently iterative process. That is, what we know about the data greatly determines our expectations, and hence, what result we would find the most interesting. With this in mind, we introduce a well-founded approach to succinctly summarizing data with a collection of itemsets; using a probabilistic maximum entropy model, we iteratively find the most interesting itemset, and in turn update our model of the data accordingly. As we only include itemsets that are surprising with regard to the current model, the summary is guaranteed to be both descriptive and non-redundant. The algorithm that we present can either mine the top-k most interesting itemsets, or use the Bayesian Information Criterion to automatically identify the model containing only the itemsets most important for describing the data. Or, in other words, it will 'tell you what you need to know'. Experiments on synthetic and benchmark data show that the discovered summaries are succinct, and correctly identify the key patterns in the data. The models they form attain high likelihoods, and inspection shows that they summarize the data well with increasingly specific, yet non-redundant itemsets.