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In this paper, we propose a two-stage multi-objective fuzzy mining algorithm for dealing with linguistic knowledge discovery. In the first stage, the multi-objective genetic algorithm is used to derive a set of non-dominated membership functions (Pareto solutions) with two objective functions. In the second stage, the clustering technique is utilized to find representative solutions from the Pareto...
The goal of data mining is to help market managers find relationships among items from large data sets to increase sales volume. The Apriori algorithm is a method for association rule mining, a data mining technique. Although a lot of mining approaches have been proposed based on the Apriori algorithm, most focus on positive association rules, such as ¡§If milk is bought, then bread is bought¡¨. However,...
In this paper, we propose an algorithm for mining high coherent utility fuzzy itemsets (HCUFI) from quantitative transactions with the properties of propositional logic. It first transforms quantitative transactions into fuzzy sets. Then, utility of each fuzzy itemsets is then calculated according to the given external utility table. If the value is large than or equals to the minimum utility ratio,...
Many fuzzy data mining approaches have been proposed for finding fuzzy association rules with the predefined minimum support from the give quantitative transactions. However, some comment problems of those approaches are that (1) a minimum support should be predefined, and it is hard to set the appropriate one, and (2) the derived rules usually expose common-sense knowledge which may not be interested...
In this paper, we handle the problem of mining fuzzy temporal association rules from a publication database, and propose an algorithm to achieve it. In the algorithm, the lifespan of an item is measured by its entire publication periods in a publication database. Also, an itemset table structure is designed to effectively keep and efficiently obtain information of itemsets for mining. Finally, experiments...
In this paper, an enhanced efficient approach for speeding up the evolution process for finding minimum supports, membership functions and fuzzy association rules is proposed by utilizing clustering techniques. All the chromosomes use the requirement satisfaction derived only from the representative chromosomes in the clusters and from their own suitability of membership functions to calculate the...
Data mining is most commonly used in attempts to induce association rules from transaction data. Most previous studies focused on binary-valued transaction data. Transaction data in real-world applications, however, usually consist of quantitative values. This paper, thus, proposes a fuzzy data-mining algorithm for extracting both association rules and membership functions from quantitative transactions...
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