An introduction to Kolmogorov complexity and its applications
An introduction to Kolmogorov complexity and its applications
Machine Learning
Beyond market baskets: generalizing association rules to correlations
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
A new framework for itemset generation
PODS '98 Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Using association rules for product assortment decisions: a case study
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Free-Sets: A Condensed Representation of Boolean Data for the Approximation of Frequency Queries
Data Mining and Knowledge Discovery
Discovering Frequent Closed Itemsets for Association Rules
ICDT '99 Proceedings of the 7th International Conference on Database Theory
Selecting the right interestingness measure for association patterns
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Interestingness of frequent itemsets using Bayesian networks as background knowledge
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Statistical and Inductive Inference by Minimum Message Length (Information Science and Statistics)
Statistical and Inductive Inference by Minimum Message Length (Information Science and Statistics)
Journal of Biomedical Informatics
Reasoning about sets using redescription mining
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Interestingness measures for data mining: A survey
ACM Computing Surveys (CSUR)
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Summarizing itemset patterns using probabilistic models
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Data Mining and Knowledge Discovery
Discovering Significant Patterns
Machine Learning
Assessing data mining results via swap randomization
ACM Transactions on Knowledge Discovery from Data (TKDD)
MINI: Mining Informative Non-redundant Itemsets
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Decomposable Families of Itemsets
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Preserving Privacy through Data Generation
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Maximum entropy based significance of itemsets
Knowledge and Information Systems
Tell me something I don't know: randomization strategies for iterative data mining
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Correlated itemset mining in ROC space: a constraint programming approach
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
ACM Transactions on Knowledge Discovery from Data (TKDD)
Computational complexity of queries based on itemsets
Information Processing Letters
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Using background knowledge to rank itemsets
Data Mining and Knowledge Discovery
Summarising data by clustering items
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part II
Krimp: mining itemsets that compress
Data Mining and Knowledge Discovery
Banded structure in binary matrices
Knowledge and Information Systems
An information theoretic framework for data mining
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Tell me what i need to know: succinctly summarizing data with itemsets
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Maximum entropy models and subjective interestingness: an application to tiles in binary databases
Data Mining and Knowledge Discovery
KDID'05 Proceedings of the 4th international conference on Knowledge Discovery in Inductive Databases
Paper: Modeling by shortest data description
Automatica (Journal of IFAC)
Randomly sampling maximal itemsets
Proceedings of the ACM SIGKDD Workshop on Interactive Data Exploration and Analytics
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Knowledge discovery from data is an inherently iterative process. That is, what we know about the data greatly determines our expectations, and therefore, what results we would find interesting and/or surprising. Given new knowledge about the data, our expectations will change. Hence, in order to avoid redundant results, knowledge discovery algorithms ideally should follow such an iterative updating procedure. With this in mind, we introduce a well-founded approach for succinctly summarizing data with the most informative itemsets; using a probabilistic maximum entropy model, we iteratively find the itemset that provides us the most novel information—that is, for which the frequency in the data surprises us the most—and in turn we update our model accordingly. As we use the maximum entropy principle to obtain unbiased probabilistic models, and only include those itemsets that are most informative with regard to the current model, the summaries we construct are guaranteed to be both descriptive and nonredundant. The algorithm that we present, called mtv, can either discover the top-k most informative itemsets, or we can employ either the Bayesian Information Criterion (bic) or the Minimum Description Length (mdl) principle to automatically identify the set of itemsets that together summarize the data well. In other words, our method will “tell you what you need to know” about the data. Importantly, it is a one-phase algorithm: rather than picking itemsets from a user-provided candidate set, itemsets and their supports are mined on-the-fly. To further its applicability, we provide an efficient method to compute the maximum entropy distribution using Quick Inclusion-Exclusion. Experiments on our method, using synthetic, benchmark, and real 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 nonredundant itemsets.