COSI-: Identification of Cosine Interesting Patterns Based on FP-tree

DOI

10.2991/icebi.2010.22How to use a DOI?

Keywords

Interestingness Measure; Cosine Measure; Conditional AntiMonotone Property; FP-tree

Abstract

The cosine similarity, also known as uncentered Pearson Correlation, has been widely used for mining association patterns, which contain objects strongly related to each other. However, it is often used as a post-evaluation measure and is computationally prohibitive for large data. To this end, we develop an FP-tree like algorithm, named COSI-tree, for finding association patterns based on the cosine measure. A key idea is to combine the strength of the FP-tree structure and the Conditional Anti-Monotone Property of the cosine measure. Experimental results on real-world data demonstrate the effectiveness of COSI-tree,inparticular for finding rare but interesting patterns at extremely low support levels.

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  - Xiaojing Huang
AU  - Junjie Wu
AU  - Shiwei Zhu
AU  - Hui Xiong
PY  - 2010/12
DA  - 2010/12
TI  - COSI-: Identification of Cosine Interesting Patterns Based on FP-tree
BT  - Proceedings of the 1st International Conference on E-Business Intelligence (ICEBI 2010)
PB  - Atlantis Press
SP  - 140
EP  - 146
SN  - 1951-6851
UR  - https://doi.org/10.2991/icebi.2010.22
DO  - 10.2991/icebi.2010.22
ID  - Huang2010/12
ER  -