An Improved Data Association Rules Mining Algorithm for Intelligent Health Surveillance

DOI

10.2991/asei-15.2015.140How to use a DOI?

Keywords

data mining; association rules; Apriori algorithm; Intelligent Health Surveillance.

Abstract

With the growing phenomenon of an aging population, Intelligent Health Surveillance technology has been developing rapidly. Meanwhile, as of things, the development of computer vision and other information technology to make rapid growth of Intelligent Health Surveillance data and diversified characteristics. Therefore, economic significance and the scientific value of the data has been an unprecedented increase. Mining association rules fully business and data, between data become the next hot spot for the Health Surveillance system to promote and applications. Due to the existing Apriori association rules data mining algorithms require to scan the Smart Health Care database many times and generate a large numbers of Health Care candidate sets, which produce giant I/O expense issues, result in low data mining computational efficiency. An improved algorithm based on the Apriori algorithm-the data association rules algorithm for intelligent health surveillance (DAR-IHS) was proposed. Under the premise of scanning database only once, we changed the storage structure of intelligent health monitoring database monitoring data and utilized binary bit operation, which greatly improved the efficiency of the algorithm and supports updating mining.

Copyright

© 2015, 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/).

Download article (PDF)

TY  - CONF
AU  - Yinghua Han
AU  - Jiaorao Liu
AU  - Yanchun Miao
PY  - 2015/05
DA  - 2015/05
TI  - An Improved Data Association Rules Mining Algorithm for Intelligent Health Surveillance
BT  - Proceedings of the 2015 International conference on Applied Science and Engineering Innovation
PB  - Atlantis Press
SP  - 730
EP  - 733
SN  - 2352-5401
UR  - https://doi.org/10.2991/asei-15.2015.140
DO  - 10.2991/asei-15.2015.140
ID  - Han2015/05
ER  -