Mining frequent patterns without candidate generation
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H-Mine: Hyper-Structure Mining of Frequent Patterns in Large Databases
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
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Managing and Mining Uncertain Data
Managing and Mining Uncertain Data
Frequent pattern mining with uncertain data
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Probabilistic frequent itemset mining in uncertain databases
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
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Mining frequent itemsets from uncertain data
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Discovering frequent subgraphs over uncertain graph databases under probabilistic semantics
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uCFS2: an enhanced system that mines uncertain data for constrained frequent sets
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Efficient discovery of frequent subgraph patterns in uncertain graph databases
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Frequent itemset mining of uncertain data streams using the damped window model
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Equivalence class transformation based mining of frequent itemsets from uncertain data
Proceedings of the 2011 ACM Symposium on Applied Computing
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Mining frequent patterns from univariate uncertain data
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A landmark-model based system for mining frequent patterns from uncertain data streams
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PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
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In this paper, we describe the development of a systematic review about the topic "Discovering Frequent Itemsets on Uncertain Data". To the best of our knowledge, this work seems to be the first systematic review addressing the topic. We show the whole process executed and its findings. Initially we define a rigorous protocol to lead the process. In the first phase, we create a systematic mapping of the area. In addition, from the complete reading of each article, a panorama of this area is presented. We reveal the search engines that most publicize this topic and which publishing types, authors and research institutions are involved in these papers. Moreover we identify the algorithms and the classes of these algorithms most compared over the years, how are made these comparisons, as well as their availabilities. Therefore this systematic review becomes a rich material for understanding this knowledge area.