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Task-Parallel FP-Growth on Cluster Computers

, 2010
Frequent itemset mining (FIM) is one of the most deeply studied data mining task. A number of algorithms, employing different approaches and advanced data structures, have already been proposed to solve the task efficiently. Even the fastest serial FIM algorithms fail to scale up with the rapid growth of database sizes.
Gülistan Özdemir Özdogan, Osman Abul
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FP-growth-algoritmi

, 2021
This bachelor's thesis studies FP-growth-algorithm, which is one of the association rule algorithm of data mining association method. Association rule algorithm finds the frequent itemsets and generates association rules based on those frequent itemsets.
Virtanen, Otto
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Quantum FP-Growth for Association Rules Mining

Quantum computing, based on quantum mechanics, promises revolutionary computational power by exploiting quantum states. It provides significant advantages over classical computing regarding time complexity, enabling faster and more efficient problem-solving.
Widad Hassina Belkadi, Yassine Drias, Habiba Drias   +2 more
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An optimized FP-growth algorithm for discovery of association rules

The Journal of Supercomputing, 2021
Association rule mining (ARM) is a data mining technique to discover interesting associations between datasets. The frequent pattern-growth (FP-growth) is an effective ARM algorithm for compressing information in the tree structure. However, it tends to suffer from the performance gap when processing large databases because of its mining procedure ...
Mai Shawkat, Mahmoud Badawi, Sally M. El-Ghamrawy, Reham Arnous, Ali I. Eldesoky   +4 more
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A Parallel FP-Growth Algorithm Based on GPU

2017 IEEE 14th International Conference on e-Business Engineering (ICEBE), 2017
This paper proposes and implements a parallel scheme of FP-growth algorithm and implements this parallel algorithm (PFP-growth algorithm). Experimental results show that, compared with FP-growth algorithm, PFP-growth algorithm is more efficient, and the larger the data set is, the lower the support threshold is, the more remarkable the speedup is.
Hao Jiang, He Meng 0004
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Integrating Spectral-CF and FP-Growth for Recommendation

Proceedings of the 2019 2nd International Conference on E-Business, Information Management and Computer Science, 2019
In the era of information overload, both information consumers and information producers have encountered great challenges: for information consumers, it is very difficult to find information of interest from a large amount of information. The recommendation system is an important tool to resolve this contradiction.
Huaxin Zhang, Yu Liu, Keyin Cao
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Message-driven FP-growth

Proceedings of the WICSA/ECSA 2012 Companion Volume, 2012
Frequent itemset mining finds frequently occurring itemsets in transactional data. This is applied to diverse problems such as decision support, selective marketing, financial forecast and medical diagnosis. The cloud, computation as an utility service, allows us to crunch large mining problems.
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Balanced parallel FP-Growth with MapReduce

2010 IEEE Youth Conference on Information, Computing and Telecommunications, 2010
Frequent itemset mining (FIM) plays an essential role in mining associations, correlations and many other important data mining tasks. Unfortunately, as the volume of dataset gets larger day by day, most of the FIM algorithms in literature become ineffective due to either too huge resource requirement or too much communication cost.
null Le Zhou, null Zhiyong Zhong, null Jin Chang, null Junjie Li, null Joshua Zhexue Huang, null Shengzhong Feng   +5 more
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