Efficiently mining long patterns from databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Discovering Frequent Closed Itemsets for Association Rules
ICDT '99 Proceedings of the 7th International Conference on Database Theory
Mining All Non-derivable Frequent Itemsets
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
Summarizing itemset patterns: a profile-based approach
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Mining compressed frequent-pattern sets
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Effective and efficient itemset pattern summarization: regression-based approaches
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
A Survey of Uncertain Data Algorithms and Applications
IEEE Transactions on Knowledge and Data Engineering
Frequent pattern mining with uncertain data
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Probabilistic frequent itemset mining in uncertain databases
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
CP-summary: a concise representation for browsing frequent itemsets
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining frequent itemsets from uncertain data
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
A tree-based approach for frequent pattern mining from uncertain data
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Mining uncertain data with probabilistic guarantees
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Accelerating probabilistic frequent itemset mining: a model-based approach
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Approximation of Frequentness Probability of Itemsets in Uncertain Data
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Mining probabilistic frequent closed itemsets in uncertain databases
Proceedings of the 49th Annual Southeast Regional Conference
Fast approximation of probabilistic frequent closed itemsets
Proceedings of the 50th Annual Southeast Regional Conference
Discovering Threshold-based Frequent Closed Itemsets over Probabilistic Data
ICDE '12 Proceedings of the 2012 IEEE 28th International Conference on Data Engineering
Finding minimum representative pattern sets
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining frequent itemsets over uncertain databases
Proceedings of the VLDB Endowment
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Mining probabilistic frequent patterns from uncertain data has received a great deal of attention in recent years due to the wide applications. However, probabilistic frequent pattern mining suffers from the problem that an exponential number of result patterns are generated, which seriously hinders further evaluation and analysis. In this paper, we focus on the problem of mining probabilistic representative frequent patterns (P-RFP), which is the minimal set of patterns with adequately high probability to represent all frequent patterns. Observing the bottleneck in checking whether a pattern can probabilistically represent another, which involves the computation of a joint probability of the supports of two patterns, we introduce a novel approximation of the joint probability with both theoretical and empirical proofs. Based on the approximation, we propose an Approximate P-RFP Mining (APM) algorithm, which effectively and efficiently compresses the set of probabilistic frequent patterns. To our knowledge, this is the first attempt to analyze the relationship between two probabilistic frequent patterns through an approximate approach. Our experiments on both synthetic and real-world datasets demonstrate that the APM algorithm accelerates P-RFP mining dramatically, orders of magnitudes faster than an exact solution. Moreover, the error rate of APM is guaranteed to be very small when the database contains hundreds transactions, which further affirms APM is a practical solution for summarizing probabilistic frequent patterns.