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In this paper, we propose an automated method to decide on the number of fuzzy sets and for the autonomous mining of both fuzzy sets and fuzzy association rules. We compare the proposed multiobjective GA based approach with: 1) CURE based approach; 2) Chien et al. (2001) clustering approach. Experimental results on JOOK transactions extracted from the adult(More)
This paper proposes a novel approach for mining fuzzy weighted multi-cross-level association rules by simply integrating the advantages of several concepts, including fuzziness, cross-level mining, weighted mining and linguistic terms for minimum support, minimum confidence and item importance. Experimental results conducted on a synthetic database(More)
For applications where the nature of the data is evolving, i.e., when database is updated at regular time intervals with insertion and deletion of records; maintenance of mining models is obviously the desired alternative if possible. Several algorithms have been proposed that can maintain minng models and can update the association rules if the data base(More)
Data warehousing is an emerging technology that facilitates gathering and integrating heterogeneous data from distributed sources and extracting information that can be utilized as a knowledge base for decision support. Once a data warehouse is built, we need to maintain it consistent with the underlying data sources, which always subject to dynamic(More)
In recent years, rapidly accumulating genomic data have posed a challenge to integrate multiple data sources and to analyze the integrated networks globally. In this paper we present a method to reverse engineer integrative gene networks. The main advantage of our method is the integration of different quantitative and qualitative data sets in order to(More)
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