Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
MAFIA: A Maximal Frequent Itemset Algorithm for Transactional Databases
Proceedings of the 17th International Conference on Data Engineering
Assessing data mining results via swap randomization
ACM Transactions on Knowledge Discovery from Data (TKDD)
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
Mixture modeling of DNA copy number amplification patterns in cancer
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Compact and understandable descriptions of mixtures of Bernoulli distributions
IDA'07 Proceedings of the 7th international conference on Intelligent data analysis
Patterns from multiresolution 0-1 data
Proceedings of the ACM SIGKDD Workshop on Useful Patterns
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Measurements in biology are made with high throughput and high resolution techniques often resulting in data in multiple resolutions. Currently, available standard algorithms can only handle data in one resolution. Generative models such as mixture models are often used to model such data. However, significance of the patterns generated by generative models has so far received inadequate attention. This paper analyses the statistical significance of the patterns preserved in sampling between different resolutions and when sampling from a generative model. Furthermore, we study the effect of noise on the likelihood with respect to the changing resolutions and sample size. Finite mixture of multivariate Bernoulli distribution is used to model amplification patterns in cancer in multiple resolutions. Statistically significant itemsets are identified in original data and data sampled from the generative models using randomization and their relationships are studied. The results showed that statistically significant itemsets are effectively preserved by mixture models. The preservation is more accurate in coarse resolution compared to the finer resolution. Furthermore, the effect of noise on data on higher resolution and with smaller number of sample size is higher than the data in lower resolution and with higher number of sample size.