Authors:
Roxana Danger
1
;
José Ruíz-Shulcloper
2
and
Rafael Berlanga
3
Affiliations:
1
University of Oriente, Cuba
;
2
Institute of Cybernetics, Mathematics and Physics, Cuba
;
3
Jaume I University, Spain
Keyword(s):
Data-mining algorithms, Association rules, Complex objects.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Business Analytics
;
Data Engineering
;
Data Mining
;
Databases and Information Systems Integration
;
Datamining
;
Enterprise Information Systems
;
Health Information Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
Abstract:
Since their introduction in 1993, association rules have been successfully applied to the description and summarization of discovered relations between attributes in a large collection of objects. However, most of the research works in this area have focused on mining simple objects, usually represented as a set of binary variables. The proposed work presents a framework for mining complex objects, whose attributes can be of any data type (single and multi-valued). The mining process is guided by the semantics associated to each object feature, which is stated by users by providing both a comparison criterion and a similarity function over the object subdescriptions. Experimental results show the usefulness of the proposal.