Authors:
Veronica Oliveira de Carvalho
1
;
Solange Oliveira Rezende
2
and
Mário de Castro
2
Affiliations:
1
Centro Universitário de Araraquara, Brazil
;
2
Computer and Mathematics Science Institute, São Paulo University, Brazil
Keyword(s):
Generalized association rules, objective evaluation measures, rule quality evaluation.
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:
Generalized association rules are rules that contain some background knowledge giving a more general view of the domain. This knowledge is codified by a taxonomy set over the data set items. Many researches use taxonomies in different data mining steps to obtain generalized rules. So, this work initially presents an approach to obtain generalized association rules in the post-processing data mining step using taxonomies. However, an important issue that has to be explored is the quality of the knowledge expressed by generalized rules, since the objective of the data mining process is to obtain useful and interesting knowledge to support the user’s decisions. In general, what researches do to help the users to select these pieces of knowledge is to reduce the obtained set by pruning some specialized rules using a subjective measure. In this context, this paper also presents a quality analysis of the generalized association rules. The quality of the rules obtained by the proposed appro
ach was evaluated. The experiments show that some knowledge evaluation objective measures are appropriate only when the generalization occurs on one specific side of the rules.
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