Abstract-Association rule are important to retailers as a source of knowledge to manage shelf, to plan an effective promotion, and so on. However, when we are mining with association rule discovery technique, we normally obtain a large number of rules. To select only good rule is difficult. Therefore, in this paper we propose the fuzzy search technique to discover interesting association rule. The comparative result of fuzzy versus non-fuzzy searches are presented in the experiment section. We found that fuzzy search is more flexible than the non-fuzzy one in finding highly constrained rules.Index Terms-Fuzzy set, fuzzy search, membership function, association rule mining. I. INTRODUCTIONAssociation rule mining is a method to discover the patterns of information, such as the pattern to reveal that there are many people coming in the supermarket to buy some specific set of products. Therefore, the owner would like to know the buying patterns of customers. The owner should perform by 2 steps, first step, to records the purchase of individual customers in the tables. Second step, when get enough information then bring it to association rule mining and then the results are association rules. This method is called "Market Basket Analysis" and this association rules are usually used in business [1]. Therefore, to select the appropriate association rules to apply, it is necessarily very much and the researcher [2] proposes an algorithm to search with many constraints and can be reduced the search space.But the most researchers continue to straightforward search association rules with normal constraint, For example if the user required support value to be equal 1.0 and items in the "then" must be X (The variable X means items that the user wants.) [3]-[6] this searching technique is less efficient than fuzzy searching technique because the results must be support value as 1.0 only (Which makes it does not received the close results, such as support 0.99 but the fuzzy searching technique may be received the results with that support 0.99.). The Fuzzy set is very popular in a variety of major because it can indicate the level of what is uncertain. There are many researchers used it during processing, such as a data support system for sales promotion analysis using fuzzy query [7] this work used technical fuzzy and used probability to search sales information by SQL language. Which the Manuscript received December 13, 2013; revised March 1, 2014. This work was supported in part by grant from Suranaree University of Technology through the funding of Data Engineering Research Unit.The authors are with the School of Computer Engineering, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand (e-mail: Zaguraba_ii@hotmail.com).original searching was not able to searched some information but fuzzy searching and weight with probability they can do it. There are many tasks related to using SQL and fuzzy in that searching [8], [9].This research proposed the method to search association rules by applying fuzzy set an...
The aim of this paper is to perform a comparative study of feature reduction techniques that are most appropriate for the classification with k-nearest neighbor and tested with medical data. Medical data are normally high-dimensional in their nature. Their high dimensionality property can affect performance of the classification process. In this work, we perform various feature reduction techniques implemented with Matlab to decrease dimensions of data before the knearest neighbor classification step. From the experimented results, we found that best performance is obtained from using the PCA algorithm to reduce features of data. The comparison in terms of accuracy turns out that PCA and ROC feature reduction techniques can improve the classification prediction, whereas the t-test feature reduction has very limited effect over the classification accuracy.
Data classification mining is a method to find data generalization in a form of rules then used these rules to predict some unknown value in the future data. But in actual applications, the rules may be of low accuracy and the number of rules may be so overwhelmed that users could not efficiently apply them. Therefore, this research proposes the development of data classification algorithm with compact fuzzy association rules to optimize accuracy and interpretability of the model. To evaluate the performance of the proposed method, this research will compare accuracy of the classification model and the number of rules against 9 different data classification algorithms. The results showed that our CCFAR algorithm is comparable in terms of accuracy. When considering both accuracy and size of model, our algorithm is the best one.
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