Results 51 to 60 of about 8,356 (188)
Incremental Frequent Itemsets Mining With FCFP Tree
IEEE Access, 2019 Frequent itemsets mining (FIM) as well as other mining techniques has been being challenged by large scale and rapidly expanding datasets. To address this issue, we propose a solution for incremental frequent itemsets mining using a Full Compression ...
Jiaojiao Sun +3 more
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An association rule-based approach for frequent item mining of multi-stage access data
Discover ComputingThe processing of large-scale datasets is complex and requires high efficiency. The database needs to be scanned multiple times by traditional Apriori algorithms to generate candidate itemsets, resulting in significantly reduced efficiency, but also have
Silong Wu
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A Model-Based Frequency Constraint for Mining Associations from Transaction Data
, 2006 Mining frequent itemsets is a popular method for finding associated items in databases. For this method, support, the co-occurrence frequency of the items which form an association, is used as the primary indicator of the associations's significance.
Hahsler, Michael
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Mining frequent itemsets with convertible constraints [PDF]
Proceedings 17th International Conference on Data Engineering, 2002 Recent work has highlighted the importance of the constraint based mining paradigm in the context of frequent itemsets, associations, correlations, sequential patterns, and many other interesting patterns in large databases. The authors study constraints which cannot be handled with existing theory and techniques.
null Jian Pei +2 more
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GPU-Accelerated Apriori Algorithm
ITM Web of Conferences, 2017 This paper propose a parallel Apriori algorithm based on GPU (GPUApriori) for frequent itemsets mining, and designs a storage structure using bit table (BIT) matrix to replace the traditional storage mode. In addition, parallel computing scheme on GPU is
Jiang Hao +3 more
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Efficient Analysis of Pattern and Association Rule Mining Approaches
, 2014 The process of data mining produces various patterns from a given data source. The most recognized data mining tasks are the process of discovering frequent itemsets, frequent sequential patterns, frequent sequential rules and frequent association rules.
Lazzez, Amor, Slimani, Thabet
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Efficiently mining maximal frequent itemsets
Proceedings 2001 IEEE International Conference on Data Mining, 2002 We present GenMax, a backtracking search based algorithm for mining maximal frequent itemsets. GenMax uses a number of optimizations to prune the search space. It uses a novel technique called progressive focusing to perform maximality checking, and diffset propagation to perform fast frequency computation.
K. Gouda, M.J. Zaki
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Frequent-Itemset Mining Using Locality-Sensitive Hashing [PDF]
, 2016 The Apriori algorithm is a classical algorithm for the frequent itemset mining problem. A significant bottleneck in Apriori is the number of I/O operation involved, and the number of candidates it generates. We investigate the role of LSH techniques to overcome these problems, without adding much computational overhead. We propose randomized variations
Bera, Debajyoti, Pratap, Rameshwar
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Inverted Index Automata Frequent Itemset Mining for Large Dataset Frequent Itemset Mining
IEEE AccessFrequent itemset mining (FIM) faces significant challenges with the expansion of large-scale datasets. Traditional algorithms such as Apriori, FP-Growth, and Eclat suffer from poor scalability and low efficiency when applied to modern datasets characterized by high dimensionality and high-density features.
Xin Dai +3 more
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Reductions for Frequency-Based Data Mining Problems
, 2017 Studying the computational complexity of problems is one of the - if not the - fundamental questions in computer science. Yet, surprisingly little is known about the computational complexity of many central problems in data mining. In this paper we study
Miettinen, Pauli, Neumann, Stefan
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