The Study of Improved FP-Growth Algorithm in MapReduce

DOI

10.2991/ccis-13.2013.58How to use a DOI?

Keywords

FP-Growth, IFP algorithm, MapReduce Introduction (Heading 1)

Abstract

As FP-Growth algorithm generates a great deal of conditional pattern bases and conditional pattern trees, leading to low efficiency, propose an improved FP-Growth(IFP) algorithm which firstly combine the same patterns based on the situation whether the support of the transaction is greater than the minimum support(min_sup) to mine the frequent patterns. Thus the IFP cuts down on the space and improves the efficiency. It also makes it easy to be paralleled. Further more, combine the IFP algorithm with the MapReduce computing model, named MR-IFP(MapReduce-Improved FP), to improve the capability to deal with the mass data.

Copyright

© 2013, 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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TY  - CONF
AU  - Sun Hong
AU  - Zhang Huaxuan
AU  - Chen Shiping
AU  - Hu Chunyan
PY  - 2013/11
DA  - 2013/11
TI  - The Study of Improved FP-Growth Algorithm in MapReduce
BT  - Proceedings of the The 1st International Workshop on Cloud Computing and Information Security
PB  - Atlantis Press
SP  - 250
EP  - 253
SN  - 1951-6851
UR  - https://doi.org/10.2991/ccis-13.2013.58
DO  - 10.2991/ccis-13.2013.58
ID  - Hong2013/11
ER  -