It is not an easy task to know a priori the most appropriate fuzzy sets that cover the domains of quantitative attributes for fuzzy association rules mining, simply because characteristics of quantitative data are in general unknown. Besides, it is unrealistic that the most appropriate fuzzy sets can always be provided by domain experts. Motivated by this, in this paper we propose an automated method for mining fuzzy association rules. For this purpose, we first present a genetic algorithm (GA) based clustering method that adjusts centroids of the clusters, which are to be handled later as midpoints of triangular membership functions. Next, we give a different method for generating the membership functions by using Clustering Using Representatives (CURE) clustering algorithm, which is known as one of the most efficient clustering algorithms described in the literature. Finally, we compared the proposed GA-based approach with other approaches from the literature. Experiments conducted on 100K transactions from the US census in the year 2000 show that the proposed method exhibits a good performance in terms of execution time and interesting fuzzy association rules.

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In the past few years, there has been an exponential increase in the amount of information available on the World Wide Web. This plethora of information can be extremely beneficial for users. However, the amount of human intervention that is currently required for this is inconvenient. Information extraction (IE) systems try to solve this problem by making the task as automatic as possible. Most of the existing approaches, however, require user feedback in one form or another during the extraction. This paper proposes a system that employs clustering techniques for automatic IE from HTML documents containing semistructured data. Using domain-specific information provided by the user, the proposed system parses and tokenizes the data from an HTML document, partitions it into clusters containing similar elements, and estimates an extraction rule based on the pattern of occurrence of data tokens. The extraction rule is then used to refine clusters, and finally, the output is reported. We employed a multiobjective genetic-algorithm-based clustering approach in the process; it is capable of finding the number of clusters and the most natural clustering. The proposed approach is tested by conducting experiments on a number of Web sites from different domains. To demonstrate the effectiveness of this approach, the results of the experiments are tested against those reported in the literature, and prove comparable.

In this paper, we propose an automated clustering method based on multi-objective genetic algorithms (GA)