Mining first-order knowledge bases for association rules

Data mining from relational databases has recently become a popular way of discovering hidden knowledge. Methods such as association rules, chi square rules, ratio rules, implication rules, etc. that have been proposed in several contexts offer complimentary choices in rule induction in this model....

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Bibliographic Details
Published in:Tools with Artificial Intelligence (ICTAI 2001): Proceedings of the 13th IEEE International Conference pp. 218 - 227
Main Author: Jamil, H.M.
Format: Conference Proceeding
Language:English
Published: IEEE 2001
Subjects:
Artificial intelligence
Association rules
Character generation
Data mining
ISBN:0769514170, 9780769514178
Online Access:Get full text
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Summary:Data mining from relational databases has recently become a popular way of discovering hidden knowledge. Methods such as association rules, chi square rules, ratio rules, implication rules, etc. that have been proposed in several contexts offer complimentary choices in rule induction in this model. Other than inductive and abductive logic programming, research into data mining from knowledge bases has been almost non-existent, because contemporary methods involve inherent procedurality which is difficult to cast into the declarativity of knowledge base systems. In this paper, we propose a logic-based technique for association rule mining from declarative knowledge which does not rely on procedural concepts such as candidate generation. This development is significant as this empowers the users with the capability to explore knowledge bases by mining association rules in a declarative and ad hoc fashion.
ISBN:0769514170
9780769514178
DOI:10.1109/ICTAI.2001.974468