Results 171 to 180 of about 3,752,831 (214)

An efficient parallel FP-Growth algorithm

2009 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, 2009
FP-Growth algorithm recursively generates huge amounts of conditional pattern bases and conditional FP-trees when the dataset is huge. In such a case, both the memory usage and computational cost are expensive, such that, the FP-tree can not meet the memory requirement. In this work, we propose a novel parallel FP-Growth algorithm, which is designed to
Min Chen, XueDong Gao, HuiFei Li
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FP Growth Application for the Prediction of Terrorist Attacks

2021
The present work partakes in the contemporary efforts to investigate AI applications to the study of terrorism. First, we introduce the contemporary debate on AI and security and review the literature presenting machine learning applications to counter-terrorisms.
Luisa Franchina, Federico Sergiani, Giulia Brutti, Federica Donati   +3 more
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SQL Based Frequent Pattern Mining with FP-Growth

2005
Scalable data mining in large databases is one of today’s real challenges to database research area. The integration of data mining with database systems is an essential component for any successful large-scale data mining application. A fundamental component in data mining tasks is finding frequent patterns in a given dataset.
Xuequn Shang 0001, Kai-Uwe Sattler, Ingolf Geist   +2 more
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Paths sharing based FP-growth data mining algorithms

2016 8th International Conference on Wireless Communications & Signal Processing (WCSP), 2016
Due to the network alarm data in cloud environment has the characteristics of massive, redundancy, relevance, etc., traditional FP-Growth algorithm has memory and computing time double bottleneck. Therefore, this paper presents an improved FP-Growth algorithm, which based on sharing path.
Shandong Ji, Dengyin Zhang, Liu Zhang
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Plagiarism Detection in Students’ Answers Using FP-Growth Algorithm

2021
According to statistics, over the past year, the quality of education has fallen due to the pandemic, and the percentage of plagiarism in the work of students has increased. Modern plagiarism detection systems work well with external plagiarism, they allow to weed out works and answers that completely copy someone else’s published ideas.
Sabina Nurlybayeva, Iskander Akhmetov, Alexander F. Gelbukh, Rustam Mussabayev   +3 more
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The Research on the Improvement of FP-growth Algorithm

Artificial Intelligence Technology Research
This paper focuses on the issue of low efficiency in the FP-growth algorithm for frequent pattern mining and proposes an improved algorithm, ICFM-growth. Experimental results demonstrate that the improved algorithm outperforms the FP-growth algorithm in terms of both runtime and space utilization.
Zixian Wu, Gang Fang
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Top Down FP-Growth for Association Rule Mining

2002
In this paper, we propose an efficient algorithm, called TD-FP-Growth (the shorthand for Top-Down FP-Growth), to mine frequent patterns. TD-FP-Growth searches the FP-tree in the top-down order, as opposed to the bottom-up order of previously proposed FP-Growth.
Ke Wang 0001, Liu Tang, Jiawei Han 0001, Junqiang Liu   +3 more
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Research on the improvement of FP-growth based on hash

The 2nd International Conference on Information Science and Engineering, 2010
FP-growth is a typical algorithm which will not generate candidate itemsets in association data mining. However, in the process of building FP-tree, each node will compare with the original, which greatly affected the efficiency of the algorithm. Based on the analysis of the process of building FP-tree, an optimization method based on hash was proposed
null Deng Yan-gu, null She Ying, null Jia Wei-qiang   +2 more
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Complexity Analysis of Depth First and FP-Growth Implementations of APRIORI

2007
We examine the complexity of Depth First and FP-growth implementations of Apriori, two of the fastest known data mining algorithms to find frequent itemsets in large databases. We describe the algorithms in a similar style, derive theoretical formulas, and provide experiments on both synthetic and real life data to illustrate the theory.
Kosters, W, Pijls, Wim, Popova, VN (Viara)   +2 more
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An Improved FP-Growth Algorithm Based on SOM Partition

2017
FP-growth algorithm is an algorithm for mining association rules without generating candidate sets. It has high practical value in many fields. However, it is a memory resident algorithm, and can only handle small data sets. It seems powerless when dealing with massive data sets. This paper improves the FP-growth algorithm.
Kuikui Jia, Haibin Liu
openaire   +1 more source