MAFIA: A Maximal Frequent Itemset Algorithm for Transactional Databases
Proceedings of the 17th International Conference on Data Engineering
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VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Probabilistic Models for Bacterial Taxonomy
Probabilistic Models for Bacterial Taxonomy
Beyond Independence: Probabilistic Models for Query Approximation on Binary Transaction Data
IEEE Transactions on Knowledge and Data Engineering
Mixture modeling of DNA copy number amplification patterns in cancer
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
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ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
Patterns from multiresolution 0-1 data
Proceedings of the ACM SIGKDD Workshop on Useful Patterns
Preservation of statistically significant patterns in multiresolution 0-1 data
PRIB'10 Proceedings of the 5th IAPR international conference on Pattern recognition in bioinformatics
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Proceedings of the 5th International Conference on PErvasive Technologies Related to Assistive Environments
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Finite mixturemodels can be used in estimating complex, unknown probability distributions and also in clustering data. The parameters of the models form a complex representation and are not suitable for interpretation purposes as such. In this paper, we present a methodology to describe the finite mixture of multivariate Bernoulli distributions with a compact and understandable description. First, we cluster the data with the mixture model and subsequently extract the maximal frequent itemsets from the cluster-specific data sets. The mixture model is used to model the data set globally and the frequent itemsets model the marginal distributions of the partitioned data locally. We present the results in understandable terms that reflect the domain properties of the data. In our application of analyzing DNA copy number amplifications, the descriptions of amplification patterns are represented in nomenclature used in literature to report amplification patterns and generally used by domain experts in biology and medicine.