Results 191 to 200 of about 846,106 (226)

Finding frequent itemsets by transaction mapping

Proceedings of the 2005 ACM symposium on Applied computing, 2005
In this paper, we present a novel algorithm for mining complete frequent itemsets. This algorithm is referred to as the TM algorithm from hereon. In this algorithm, we employ the vertical representation of a database. Transaction ids of each itemset are mapped and compressed to continuous transaction intervals in a different space thus reducing the ...
Mingjun Song, Sanguthevar Rajasekaran
openaire   +1 more source

Incremental Frequent Itemsets Mining with MapReduce

2017
Frequent itemsets mining is a common task in data mining. Since sizes of today’s databases go far beyond capabilities of a single machine, recent studies show how to adopt classical algorithms for frequent itemsets mining for parallel frameworks such as MapReduce. Even then, in case of a slight database update a re-run of the MapReduce mining algorithm
Kirill Kandalov, Ehud Gudes
openaire   +2 more sources

Exploring Frequent Itemsets in Sweltering Climates

2019
With digital transformation and in the highly competitive retail market, it is important to understand customer needs and environmental changes. Moreover, obtain more profits through novel data mining technology is essential as well. Thus, the following questions should be addressed. Does climate influence the purchasing willingness of consumers?
Ping Yu Hsu 0001, Chen Wan Huang, Ming-Shien Cheng, Yen-Huei Ko, Cheng-Han Tsai, Ni Xu   +5 more
openaire   +1 more source

Mining Frequent Itemsets with Dualistic Constraints

2012
Mining frequent itemsets can often generate a large number of frequent itemsets. Recent studies proposed mining itemset with the different types of constraint. The paper is to mine frequent itemsets, where a one: does not contain any item of C0 or contains at least one item of C0.
Anh N. Tran, Hai V. Duong, Tin C. Truong, Bac Le   +3 more
openaire   +2 more sources

FPGA/GPU-based Acceleration for Frequent Itemsets Mining: A Comprehensive Review

ACM Computing Surveys, 2022
Martin Letras-Luna, José Hernández-Palancar, Claudia Feregrino-Uribe   +2 more
exaly  

Efficient strategies for incremental mining of frequent closed itemsets over data streams

Expert Systems With Applications, 2022
Xiaoning Jiang, Junqiang Liu, Xueling Wang   +2 more
exaly  

Frequent Itemsets

2020
openaire   +1 more source

Discovery of maximum length frequent itemsets

Information Sciences, 2008
Tianming Hu
exaly  

An efficient method for mining frequent itemsets with double constraints

Engineering Applications of Artificial Intelligence, 2014
Bay Vo, Hai Duong, Tin Truong
exaly  

A novel pruning algorithm for mining long and maximum length frequent itemsets

Expert Systems With Applications, 2020
Sina Lessanibahri, Luca Gastaldi
exaly