scholarly journals A Comparative Analysis of Frequent Pattern Mining Algorithms

2017 ◽  
Vol V (XI) ◽  
pp. 3010-3012
Author(s):  
Heli Vyas
Keyword(s):  
Comparative Analysis ◽  
Pattern Mining ◽  
Frequent Pattern Mining ◽  
Frequent Pattern ◽  
Mining Algorithms

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

Clustering of Time Series Data

2011 ◽  
pp. 258-263
Author(s):  
Anne Denton
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Pattern Mining ◽  
Time Series Data ◽  
Frequent Pattern Mining ◽  
Frequent Pattern ◽  
Series Data ◽  
Science And Engineering ◽  
Data Mining Algorithms ◽  
Mining Algorithms

Time series data is of interest to most science and engineering disciplines and analysis techniques have been developed for hundreds of years. There have, however, in recent years been new developments in data mining techniques, such as frequent pattern mining, that take a different perspective of data. Traditional techniques were not meant for such pattern-oriented approaches. There is, as a result, a significant need for research that extends traditional time-series analysis, in particular clustering, to the requirements of the new data mining algorithms.

scholarly journals Frequent Pattern Mining Algorithms for Finding Associated Frequent Patterns for Data Streams: A Survey

2014 ◽  
Vol 37 ◽  
pp. 109-116 ◽  
Author(s):  
Shamila Nasreen ◽  
Muhammad Awais Azam ◽  
Khurram Shehzad ◽  
Usman Naeem ◽  
Mustansar Ali Ghazanfar
Keyword(s):  
Data Streams ◽  
Pattern Mining ◽  
Frequent Pattern Mining ◽  
Frequent Pattern ◽  
Frequent Patterns ◽  
Mining Algorithms

scholarly journals Distributed frequent hierarchical pattern mining for robust and efficient large-scale association discovery

2017 ◽  
Author(s):  
◽  
Michael Phinney
Keyword(s):  
Data Mining ◽  
Distributed Computing ◽  
Pattern Mining ◽  
Frequent Pattern Mining ◽  
Frequent Pattern ◽  
Generation Process ◽  
Computing Environment ◽  
Wide Range ◽  
Mining Algorithms ◽  
Hierarchical Pattern

Frequent pattern mining is a classic data mining technique, generally applicable to a wide range of application domains, and a mature area of research. The fundamental challenge arises from the combinatorial nature of frequent itemsets, scaling exponentially with respect to the number of unique items. Apriori-based and FPTree-based algorithms have dominated the space thus far. Initial phases of this research relied on the Apriori algorithm and utilized a distributed computing environment; we proposed the Cartesian Scheduler to manage Apriori's candidate generation process. To address the limitation of bottom-up frequent pattern mining algorithms such as Apriori and FPGrowth, we propose the Frequent Hierarchical Pattern Tree (FHPTree): a tree structure and new frequent pattern mining paradigm. The classic problem is redefined as frequent hierarchical pattern mining where the goal is to detect frequent maximal pattern covers. Under the proposed paradigm, compressed representations of maximal patterns are mined using a top-down FHPTree traversal, FHPGrowth, which detects large patterns before their subsets, thus yielding significant reductions in computation time. The FHPTree memory footprint is small; the number of nodes in the structure scales linearly with respect to the number of unique items. Additionally, the FHPTree serves as a persistent, dynamic data structure to index frequent patterns and enable efficient searches. When the search space is exponential, efficient targeted mining capabilities are paramount; this is one of the key contributions of the FHPTree. This dissertation will demonstrate the performance of FHPGrowth, achieving a 300x speed up over state-of-the-art maximal pattern mining algorithms and approximately a 2400x speedup when utilizing FHPGrowth in a distributed computing environment. In addition, we allude to future research opportunities, and suggest various modifications to further optimize the FHPTree and FHPGrowth. Moreover, the methods we offer will have an impact on other data mining research areas including contrast set mining as well as spatial and temporal mining.

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