Multiple Data Source Discovery with Group Interaction Approach
- https://doi.org/10.2991/iccsee.2013.707How to use a DOI?
- Data mining, multiple data source mining, interaction, difference detection
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.
- © 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/).
Cite this article
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 - https://doi.org/10.2991/iccsee.2013.707 ID - Wu2013/03 ER -