Mining Maximal Frequent Patterns With Similarity Matrices of Data Records

DOI

10.2991/icebi.2010.20How to use a DOI?

Keywords

Data mining; Maximal frequent pattern; Similarity matrix;

Abstract

In this paper, we proposed a similarity matrix based method to mining maximal frequent patterns from large database. The study is very different from the previous Apriori-liked method. Especially, the method can be performed directly on the original data in database without various format transformation. The analyzing and experimental results show that the method is useful for frequent pattern mining tasks with large data set.

Copyright

© 2010, 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  - Hua Yuan
AU  - Junjie Wu
PY  - 2010/12
DA  - 2010/12
TI  - Mining Maximal Frequent Patterns With Similarity Matrices of Data Records
BT  - Proceedings of the 1st International Conference on E-Business Intelligence (ICEBI 2010)
PB  - Atlantis Press
SP  - 124
EP  - 131
SN  - 1951-6851
UR  - https://doi.org/10.2991/icebi.2010.20
DO  - 10.2991/icebi.2010.20
ID  - Yuan2010/12
ER  -