Balancing the Analysis of Frequent Patterns

2014 ◽  
pp. 53-64 ◽  
Author(s):  
Arnaud Giacometti ◽  
Dominique H. Li ◽  
Arnaud Soulet
Keyword(s):  
Frequent Patterns

Mining Top-K of Stream Frequent Patterns

2018 ◽  
pp. -1--1
Author(s):  
EL-Mehdi Ali ◽  
Yan-Lin He ◽  
QunXiong Zhu
Keyword(s):  
Frequent Patterns

Mining Frequent Patterns over Data Stream under the Time Decaying Model

2010 ◽  
Vol 36 (5) ◽  
pp. 674-684 ◽  
Author(s):  
Feng WU ◽  
Yan ZHONG ◽  
Quan-Yuan WU
Keyword(s):  
Data Stream ◽  
Frequent Patterns

Customized frequent patterns mining algorithms for enhanced Top-Rank-K frequent pattern mining

2021 ◽  
Vol 169 ◽  
pp. 114530
Author(s):  
Areej Ahmad Abdelaal ◽  
Sa'ed Abed ◽  
Mohammad Al-Shayeji ◽  
Mohammad Allaho
Keyword(s):  
Pattern Mining ◽  
Frequent Pattern Mining ◽  
Frequent Pattern ◽  
Frequent Patterns ◽  
Mining Algorithms

Towards Faster Mining of Disjunction-Based Concise Representations of Frequent Patterns

2014 ◽  
Vol 23 (02) ◽  
pp. 1450001
Author(s):  
T. Hamrouni ◽  
S. Ben Yahia ◽  
E. Mephu Nguifo
Keyword(s):  
Empirical Study ◽  
Real Life ◽  
Search Space ◽  
Frequent Patterns ◽  
Memory Consumption ◽  
Efficient Tool ◽  
Condensed Representation ◽  
Benchmark Datasets ◽  
Condensed Representations ◽  
Amount Of Knowledge

In many real-life datasets, the number of extracted frequent patterns was shown to be huge, hampering the effective exploitation of such amount of knowledge by human experts. To overcome this limitation, exact condensed representations were introduced in order to offer a small-sized set of elements from which the faithful retrieval of all frequent patterns is possible. In this paper, we introduce a new exact condensed representation only based on particular elements from the disjunctive search space. In this space, a pattern is characterized by its disjunctive support, i.e., the frequency of complementary occurrences – instead of the ubiquitous co-occurrence link – of its items. For several benchmark datasets, this representation has been shown interesting in compactness terms compared to the pioneering approaches of the literature. In this respect, we mainly focus here on proposing an efficient tool for mining this representation. For this purpose, we introduce an algorithm, called DSSRM, dedicated to this task. We also propose several techniques to optimize its mining time as well as its memory consumption. The carried out empirical study on benchmark datasets shows that DSSRM is faster by several orders of magnitude than the MEP algorithm.

scholarly journals An Efficient Count Based Transaction Reduction Approach for Mining Frequent Patterns

2015 ◽  
Vol 47 ◽  
pp. 52-61
Author(s):  
V. Vijayalakshmi ◽  
A. Pethalakshmi
Keyword(s):  
Frequent Patterns ◽  
Reduction Approach
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