یک روش بهبود یافته برای پیدا کردن توابع عضویت و حداقل حمایت های چندگانه در داده کاوی فازی
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|22162||2009||9 صفحه PDF||سفارش دهید||6485 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Expert Systems with Applications, Volume 36, Issue 6, August 2009, Pages 10016–10024
Fuzzy mining approaches have recently been discussed for deriving fuzzy knowledge. Since items may have their own characteristics, different minimum supports and membership functions may be specified for different items. In the past, we proposed a genetic-fuzzy data-mining algorithm for extracting minimum supports and membership functions for items from quantitative transactions. In that paper, minimum supports and membership functions of all items are encoded in a chromosome such that it may be not easy to converge. In this paper, an enhanced approach is proposed, which processes the items in a divide-and-conquer strategy. The approach is called divide-and-conquer genetic-fuzzy mining algorithm for items with Multiple Minimum Supports (DGFMMS), and is designed for finding minimum supports, membership functions, and fuzzy association rules. Possible solutions are evaluated by their requirement satisfaction divided by their suitability of derived membership functions. The proposed GA framework maintains multiple populations, each for one item’s minimum support and membership functions. The final best minimum supports and membership functions in all the populations are then gathered together to be used for mining fuzzy association rules. Experimental results also show the effectiveness of the proposed approach.
Data mining is commonly used for inducing association rules from transaction data. An association rule is an expression X → Y, where X is a set of items and Y is a single item. It means in the set of transactions, if all the items in X exist in a transaction, then Y is also in the transaction with a high probability ( Agrawal & Srikant, 1994). Most previous studies focused on binary-valued transaction data. Transaction data in real-world applications, however, usually consist of quantitative values. Designing a sophisticated data-mining algorithm able to deal with various types of data presents a challenge to workers in this research field. Fuzzy set theory has been used in intelligent systems for a long time because of its simplicity and similarity to human reasoning (Chen et al., 2000, Siler and James, 2004 and Zhang and Liu, 2006). The theory has been applied in fields such as manufacturing, engineering, diagnosis, economics, among others (Heng et al., 2006, Ishibuchi and Yamamoto, 2005 and Liang et al., 2002). Several fuzzy learning algorithms for inducing rules from given sets of data have been designed and used to good effect with specific domains (Casillas et al., 2005, Hong et al., 2001 and Rasmani and Shen, 2004). Most of the previous approaches set a single minimum support threshold for all the items or itemsets and identify the relationships among binary transactions. In real applications, different items may have different criteria to judge their importance and quantitative data may exist. We can thus divide the fuzzy data mining approaches into two kinds, namely single-minimum-support fuzzy-mining (SSFM) and multiple-minimum-support fuzzy-mining (MSFM) problems. Several mining approaches ( Chan and Au, 1997, Hong et al., 1999, Hong et al., 2001, Kuok et al., 1998 and Yue et al., 2000) have been proposed for the SSFM problem. Chan and Au proposed an F-APACS algorithm to mine fuzzy association rules ( Chan & Au, 1997). They first transformed quantitative attribute values into linguistic terms and then used the adjusted difference analysis to find interesting associations among attributes. Kuok et al. (1998) proposed a fuzzy mining approach to handle numerical data in databases and derived fuzzy association rules. At nearly the same time, Hong et al. (1999) proposed a fuzzy mining algorithm to mine fuzzy rules from quantitative transaction data. Basically, these fuzzy mining algorithms first used membership functions to transform each quantitative value into a fuzzy set in linguistic terms and then used a fuzzy mining process to find fuzzy association rules. Yue et al. (2000) then extended the above concept to find fuzzy association rules with weighted items from transaction data. They adopted Kohonen self-organized mapping to derive fuzzy sets for numerical attributes. As to the MSFM problem, Lee, Hong, and Lin (2004) proposed a mining algorithm which used multiple minimum supports to mine fuzzy association rules. They assumed that items had different minimum supports and the minimum support for an itemset was set as the maximum of the minimum supports of the items contained in the itemset. Under the constraint, the characteristic of level-by-level processing was kept, such that the original Apriori algorithm could easily be extended to finding large itemsets. In the above approaches, the membership functions were assumed to be known in advance. Although many approaches for learning membership functions were proposed (Cordón et al., 2001, Roubos and Setnes, 2001, Setnes and Roubos, 2000, Wang et al., 1998 and Wang et al., 2000), most of them were usually used for classification or control problems. For fuzzy mining problems, Kaya et al. (2003) proposed a GA-based approach to derive a predefined number of membership functions for getting a maximum profit within a user specified interval of minimum supports. Hong, Chen, Wu, and Lee (2006) also proposed a genetic-fuzzy data-mining algorithm for extracting both association rules and membership functions from quantitative transactions. It maintained a population of sets of membership functions and used the genetic algorithm to automatically derive the resulting one. Its fitness function considered the number of large 1-itemsets and the suitability of membership functions. The suitability measure was used to reduce the occurrence of bad types of membership functions. The above mentioned approaches, however, were mainly proposed for the SSFM problem. As to the MSFM problem, we proposed in the past a genetic-fuzzy mining algorithm for items with multiple minimum supports (called the GFMMS algorithm) for solving it ( Chen, Hong, Tseng, & Lee, 2009). The minimum supports and sets of membership functions of all the items were encoded into a chromosome. Each chromosome was then evaluated by the criteria of requirement satisfaction and suitability of membership functions. Since the chromosome was quite long in this way, lots of processing time was spent to learn global nearly optimal solutions. Recently, the divide-and-conquer strategy has been used in the evolutionary computation community with a very good effect. Many algorithms based on it have been proposed in different applications (Au et al., 2003, Darwen and Yao, 1997, Khare et al., 2005 and Yao, 2003). In this paper, we thus propose an enhanced GFMMS algorithm, namely divide-and-conquer genetic-fuzzy mining algorithm for items with Multiple Minimum Supports (DGFMMS), that can divide-and-conquer the derivation process of the minimum supports and membership functions of different items. The proposed algorithm maintains multiple populations, each for one item’s minimum support and membership functions. The fitness of each set of membership functions is evaluated by the requirement satisfaction which is used to reflect the closeness of the derived strength of fuzzy regions of large 1-itemsets for chromosome to its Required Strength of Fuzzy regions (RSF) and by the suitability of the derived membership functions. The final best sets of membership functions in all the populations are then gathered together to be used for mining fuzzy association rules. Experiments also were made to show the effective of the proposed approach. The remaining parts of this paper are organized as follows. The proposed divide-and-conquer genetic-fuzzy mining framework for items with multiple minimum supports is introduced in Section 2. The adjustment process of minimum supports and membership functions is explained in Section 3. The details of the proposed algorithm for mining multiple minimum supports, membership functions and fuzzy association rules are described in Section 4. An example to illustrate the proposed algorithm is given in Section 5. Experiments to demonstrate the performance of the proposed algorithm are stated in Section 6. Conclusions and future works are given in Section 7.
نتیجه گیری انگلیسی
In this paper, we have proposed a divide-and-conquer genetic-fuzzy mining algorithm for items with Multiple Minimum Supports (DGFMMS) to extract multiple minimum supports, membership functions and fuzzy association rules from quantitative transactions. It maintains multiple populations, each for one item’s minimum support and membership functions. The fitness value of a chromosome is evaluated by the requirement satisfaction and the suitability of it. The proposed approach has two advantages. The first one is that since the proposed approach executes the derivation process in a divide-and-conquer way, its fitness convergence is fast. Experimental results also show that the proposed approach (DGFMMS) is faster than the previous one (GFMMS). The second advantage is that the proposed approach (DGFMMS) can achieve similar or better results than the previous one (GFMMS). Experimental results also show this. In the future, we will continuously attempt to enhance the genetic-fuzzy mining framework for more complex problems.