Multiple Data Source Discovery with Group Interaction Approach

DOI

10.2991/iccsee.2013.707How to use a DOI?

Keywords

Data mining, multiple data source mining, interaction, difference detection

Abstract

Medical researchers seek to identify and predict profit (or effectiveness) potential in a new medicine B against a specified disease by comparing it to an existing medicine A, which has been used to treat the disease for many years, called medicine assessment. Applying traditional data mining techniques to the medicine assessment, one can discover patterns, such as A.X=a B.Y=b, which are identified at the attribute-value level. These patterns are useful in predicting associated behaviors at the attribute-value level. However, to evaluate B against A, we have to obtain globally useful relations between B and A at an attribute level. Therefore, this paper proposes a group interaction approach for multiple data source discovery. Group interactions include, such as rules, differences, and links between datasets. These group interactions are discovered at the attribute level. For example, R(A.X, B.Y), where R is a relationship, or a predication. Some examples are presented for illustrating the use of the group interaction approach.

Copyright

© 2013, 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  - Hao Wu
PY  - 2013/03
DA  - 2013/03
TI  - Multiple Data Source Discovery with Group Interaction Approach
BT  - Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013)
PB  - Atlantis Press
SP  - 2833
EP  - 2836
SN  - 1951-6851
UR  - https://doi.org/10.2991/iccsee.2013.707
DO  - 10.2991/iccsee.2013.707
ID  - Wu2013/03
ER  -