Proceedings of 2016 International Conference on Modeling, Simulation and Optimization Technologies and Applications (MSOTA2016)

The Research and Improvement Based on FP-Growth Data Mining Algorithm

Authors
Quanzhu Yao, Xingxing Gao, Xueli Lei, Tong Zhang
Corresponding Author
Quanzhu Yao
Available Online December 2016.
DOI
10.2991/msota-16.2016.62How to use a DOI?
Keywords
FP-Growth algorithm; data mining, frequent itemsets; FP-tree, ENFP-growth algorithm
Abstract

In order to improve the availability in space domain and raise time efficiency of the algorithm, increasing more FP-array to decrease the traversing of FP-tree in this paper. It has put forward to applying the node switching strategy to generate more packed FP-tree. It has come up with increasing more FP-array to decrease the traversing of FP-tree. The space of storing FP-tree would be reduced, it could avoid allocating more other space and improve the utilization factor of the space. The solution indicates that the efficiency of time and space of improved FP-Growth algorithm is higher than that of classical FP-Growth algorithm and TFP-growth algorithm. What is new and original in this paper is it applies node switching strategy to generate more packed FP-tree and increasing more FP-array to decrease the traversing of FP-tree.

Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of 2016 International Conference on Modeling, Simulation and Optimization Technologies and Applications (MSOTA2016)
Series
Advances in Computer Science Research
Publication Date
December 2016
ISBN
10.2991/msota-16.2016.62
ISSN
2352-538X
DOI
10.2991/msota-16.2016.62How to use a DOI?
Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Quanzhu Yao
AU  - Xingxing Gao
AU  - Xueli Lei
AU  - Tong Zhang
PY  - 2016/12
DA  - 2016/12
TI  - The Research and Improvement Based on FP-Growth Data Mining Algorithm
BT  - Proceedings of 2016 International Conference on Modeling, Simulation and Optimization Technologies and Applications (MSOTA2016)
PB  - Atlantis Press
SP  - 287
EP  - 293
SN  - 2352-538X
UR  - https://doi.org/10.2991/msota-16.2016.62
DO  - 10.2991/msota-16.2016.62
ID  - Yao2016/12
ER  -