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The Studies of Mining Frequent Patterns Based on Frequent Pattern Tree
2009Mining frequent patterns is to discover the groups of items appearing always together excess of a user specified threshold. Many approaches have been proposed for mining frequent pattern. However, either the search space or memory space is huge, such that the performance for the previous approach degrades when the database is massive or the threshold ...
Show-Jane Yen +4 more
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Semantic annotation of frequent patterns
ACM Transactions on Knowledge Discovery from Data, 2007Using frequent patterns to analyze data has been one of the fundamental approaches in many data mining applications. Research in frequent pattern mining has so far mostly focused on developing efficient algorithms to discover various kinds of frequent patterns, but little attention has been paid to the important next step—interpreting the discovered ...
Qiaozhu Mei +4 more
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Discovering frequent pattern pairs
Intelligent Data Analysis, 2013Cubes and association rules discover frequent patterns in a data set, most of which are not significant. Thus previous research has introduced search constraints and statistical metrics to discover significant patterns and reduce processing time. We introduce cube pairs (comparing cube groups based on a parametric statistical test) and rule pairs ...
Carlos Ordonez 0001, Zhibo Chen 0002
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Approximate mining of frequent patterns on streams [PDF]
Intelligent Data Analysis, 2007 Many critical applications, like intrusion detection or stock market analysis, require a nearly immediate result based on a continuous and infinite stream of data. In most cases finding an exact solution is not compatible with limited availability of resources and real time constraints, but an approximation of the exact result is enough for most ...
SILVESTRI, Claudio, ORLANDO, Salvatore
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Sampling Ensembles for Frequent Patterns
2005A popular solution to improving the speed and scalability of association rule mining is to do the algorithm on a random sample instead of the entire database. But it is at the expense of the accuracy of answers. In this paper, we present a sampling ensemble approach to improve the accuracy for a given sample size.
Caiyan Jia, Ruqian Lu
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Mining Frequent Patterns with Incremental Updating Frequent Pattern Tree
2006 6th World Congress on Intelligent Control and Automation, 2006Mining frequent patterns has been studied popularly in data mining research. However, very little work has been done on maintenance of mined frequent patterns. For the real useful frequent patterns, one must continually adjust a minimum support threshold. Expensive and repeated database scans were done.
null Qunxiong Zhu, null Xiaoyong Lin
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Generating Closed Frequent Itemsets with the Frequent Pattern List
2010 2nd International Workshop on Database Technology and Applications, 2010An approach is proposed to discover closed frequent itemsets with a simple linear list structure called the Frequent Pattern List(FPL) in transaction database. The approach selects representation patterns from candidate itemsets to reduce combinational space of frequent patterns.
Qin Li, Sheng Chang
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Mining Maximal Frequent Itemsets with Frequent Pattern List
Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007), 2007Mining frequent itemsets is a major aspect of association rule research. However, the mining of the complete of frequent itemsets will lead to a huge number of itemsets. Fortunately, this problem can be reduced to the mining of maximal frequent itemsets.
Jin Qian, Feiyue Ye
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Mining Supplemental Frequent Patterns
2008The process of resource distribution and load balance of a distributed P2P network can be described as the process of mining Supplement Frequent Patterns (SFPs) from query transaction database. With given minimum support (min_sup) and minimum share support (min_share_sup), each SFP includes a core frequent pattern (BFP) used to draw other frequent or ...
Yintian Liu +4 more
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Balancing the Analysis of Frequent Patterns
2014A main challenge in pattern mining is to focus the discovery on high-quality patterns. One popular solution is to compute a numerical score on how well each discovered pattern describes the data. The best rating patterns are then the most analyzed by the data expert.
Arnaud Giacometti +2 more
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