Proceedings of the 2016 International Conference on Artificial Intelligence: Technologies and Applications

A Combined Co-location Pattern Mining Approach for Post-Analyzing Co-location Patterns

Authors
Yuan Fang, Lizhen Wang, Junli Lu, Lihua Zhou
Corresponding Author
Yuan Fang
Available Online January 2016.
DOI
https://doi.org/10.2991/icaita-16.2016.10How to use a DOI?
Keywords
co-location pattern mining; combined mining; post-analysis
Abstract
The co-location pattern mining discovers the subsets of spatial features which are located together frequently in geography. However, the huge number of the co-location mining results limit the usability of co-location patterns. Furthermore, users hardly identify and understand the interesting knowledge directly from the single co-location pattern.In this paper, we studied the problem of extractingcombined co-location patterns from a large collectionof prevalent co-location patterns.We first gave the definitions of atomic co-location pattern, combined co-location pattern pair and cluster; secondly, we designed a series of interesting metrics to measure the interestingness of atomic co-location patterns, combined co-location pattern pairs and clusters; thirdly, an combined co-location mining algorithm and redundant elimination strategies were proposed. The experiments evaluated the method both on real data sets and syntheticdata sets. The results show that our method can effectively discover combined co-location patterns.
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This is an open access article distributed under the CC BY-NC license.

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Volume Title
Proceedings of the 2016 International Conference on Artificial Intelligence: Technologies and Applications
Series
Advances in Intelligent Systems Research
Publication Date
January 2016
ISBN
978-94-6252-162-9
ISSN
1951-6851
DOI
https://doi.org/10.2991/icaita-16.2016.10How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Yuan Fang
AU  - Lizhen Wang
AU  - Junli Lu
AU  - Lihua Zhou
PY  - 2016/01
DA  - 2016/01
TI  - A Combined Co-location Pattern Mining Approach for Post-Analyzing Co-location Patterns
BT  - Proceedings of the 2016 International Conference on Artificial Intelligence: Technologies and Applications
PB  - Atlantis Press
SP  - 38
EP  - 43
SN  - 1951-6851
UR  - https://doi.org/10.2991/icaita-16.2016.10
DO  - https://doi.org/10.2991/icaita-16.2016.10
ID  - Fang2016/01
ER  -