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From path tree to frequent patterns: a framework for mining frequent patterns
2002 IEEE International Conference on Data Mining, 2002. Proceedings., 2003We propose a framework for mining frequent patterns from large transactional databases. The core of the framework is a coded prefix-path tree with two representations, namely, a memory-based prefix-path tree and a disk-based prefix-path tree. The disk-based prefix-path tree is simple in its data structure yet rich in information contained, and is small
Yabo Xu +3 more
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Generating Frequent Patterns with the Frequent Pattern List
2001The generation of frequent patterns (or frequent itemsets) has been studied in various areas of data mining. Most of the studies take the Apriori-based generation-and-test approach, which is computationally costly in the generation of candidate frequent patterns.
Fan-Chen Tseng, Ching-Chi Hsu
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Mining Frequent Independent Patterns and Frequent Correlated Patterns Synchronously
2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery, 2008One of the main tasks of KDTCM (knowledge discovery in Traditional Chinese Medicine) is discovering novel paired or grouped drugs in Chinese Medical Formula (CMF) database, which are special combinations of two or more drugs. Correlation mining is much effective because of the large number of correlation relationships among various kinds of drugs ...
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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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Computational aspects of mining maximal frequent patterns [PDF]
Theoretical Computer Science, 2006 In this paper we study the complexity-theoretic aspects of mining maximal frequent patterns, from the perspective of counting the number of all distinct solutions.
Yang, Guizhen, Guizhen Yang
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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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