A Logic-Based Approach to Mining Inductive Databases

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Liu, Hong-Cheu, Yu, Jeffrey Xu, Zeleznikow, John ORCID: 0000-0002-8786-2644 and Guan, Ying (2007) A Logic-Based Approach to Mining Inductive Databases. In: Computational Science ICCS 2007: 7th International Conference, Beijing, China, May 27 - 30, 2007, Proceedings. Shi, Yong, van Albada, Geert Dick, Dongarra, Jack and Sloot, Peter M. A, eds. Lecture Notes in Computer Science , 1 (4487). Springer, Berlin, Germany, pp. 270-277.


In this paper, we discuss the main problems of inductive query languages and optimisation issues. We present a logic-based inductive query language and illustrate the use of aggregates and exploit a new join operator to model specific data mining tasks. We show how a fixpoint operator works for association rule mining and a clustering method. A preliminary experimental result shows that fixpoint operator outperforms SQL and Apriori methods. The results of our framework could be useful for inductive query language design in the development of inductive database systems.

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Item type Book Section
URI https://vuir.vu.edu.au/id/eprint/10693
DOI 10.1007/978-3-540-72584-8_35
Official URL http://download.springer.com/static/pdf/168/chp%25...
ISBN 9783540725831
Subjects Historical > RFCD Classification > 280000 Information, Computing and Communication Sciences
Historical > FOR Classification > 0801 Artificial Intelligence and Image Processing
Historical > FOR Classification > 0806 Information Systems
Historical > Faculty/School/Research Centre/Department > School of Management and Information Systems
Historical > SEO Classification > 8999 Other Information and Communication Services
Keywords ResPubID13939, relational computation, association rule mining, fixpoint computation, logic-based inductive query language, data mining query language design, inductive database system
Citations in Scopus 3 - View on Scopus
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