Frequent Itemset Mining Algorithm based on Sampling Method

DOI

10.2991/iccsae-15.2016.158How to use a DOI?

Keywords

Frequent Itemset; Sampling; Data Mining.

Abstract

Frequent itemset mining is an important technique in data mining. This paper employ the sampling method to improve the performance. An in-memory index is presented to store the data information, which is maintained by our proposed algorithm FIMS. We conduct the experiments over two datasets and find that when the sampling rate is reduced, the mining performance will be more efficient.

Copyright

© 2016, 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  - Haifeng Li
AU  - Ning Zhang
AU  - YueJin Zhang
PY  - 2016/02
DA  - 2016/02
TI  - Frequent Itemset Mining Algorithm based on Sampling Method
BT  - Proceedings of the 2015 5th International Conference on Computer Sciences and Automation Engineering
PB  - Atlantis Press
SP  - 852
EP  - 855
SN  - 2352-538X
UR  - https://doi.org/10.2991/iccsae-15.2016.158
DO  - 10.2991/iccsae-15.2016.158
ID  - Li2016/02
ER  -