Conclusion and future work

This paper has proposed a new fuzzy data-mining algorithm for extracting

interesting knowledge from object-oriented quantitative transactions. The proposed fuzzy algorithm is divided into two main phases. The first phase is called the fuzzy intra-object mining phase, in which the linguistic large itemsets associated with the same classes (items) but with different attributes are derived. The second phase is called the fuzzy inter-object mining phase, in which the large itemsets derived from the composite items are used to represent the relationship among different kinds of objects. Both the intra-object and inter-object association rules can thus be easily derived by the proposed algorithm at the same time. An example has also been given to illustrate the algorithm in detail. Experimental results have shown the effects of the parameters on the proposed algorithm. The numbers of fuzzy intra-object association rules are usually smaller than those of fuzzy inter-object association rules because the attribute number is less than the item number in real applications. Finding inter-object association rules thus spends more time than finding intra-object association rules. In the future, we will further generalize our approach to manage other different mining problems.

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