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In IGFSMS problem, many approaches have been published [2], Hong et al. proposed a genetic-fuzzy data-mining algorithm for extracting both association rules and membership functions from quantitative transactions [2]. They propose a GA-based framework for searching membership functions suitable for mining problems and then use the final best set of membership functions to mine association rules. The proposed framework is shown in Figure Fig 1: GA-based framework for searching membership functions

Figure 1 In IGFSMS problem, many approaches have been published [2], Hong et al. proposed a genetic-fuzzy data-mining algorithm for extracting both association rules and membership functions from quantitative transactions [2]. They propose a GA-based framework for searching membership functions suitable for mining problems and then use the final best set of membership functions to mine association rules. The proposed framework is shown in Figure Fig 1: GA-based framework for searching membership functions

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Table 1. An example of market basket transactions. The rule suggests that a strong relationship exists between the sale of diapers and beer because many customers who buy diapers also buy beer. Retailers can use this type of rules to help them identify new opportunities for cross selling their products to the customers.
Table 1. An example of market basket transactions. The rule suggests that a strong relationship exists between the sale of diapers and beer because many customers who buy diapers also buy beer. Retailers can use this type of rules to help them identify new opportunities for cross selling their products to the customers.
Table 2. An example of fuzzy dataset. Table 2 contains a sample fuzzy dataset. We can determine the value of the attribute i, of the j" record by using the convention t[i,]. For example, if we want to determine the value of salary of third record, we will write t3[Salary] and obtain the value 28000. In Table 1, the attribute salary has been denoted using the fuzzy set Salary = {high, normal, low}, dividing the salary interval into low, normal and high. For the interval (Rs. 10000 to Rs. 30000) we have normal salary, for (Rs. 10,000 and below) we have low salary and for (Rs.30, 000 and above )we have high salary. MH _.*. 2... kd fe 8 2 ad i nak nn ee, ee, ee
Table 2. An example of fuzzy dataset. Table 2 contains a sample fuzzy dataset. We can determine the value of the attribute i, of the j" record by using the convention t[i,]. For example, if we want to determine the value of salary of third record, we will write t3[Salary] and obtain the value 28000. In Table 1, the attribute salary has been denoted using the fuzzy set Salary = {high, normal, low}, dividing the salary interval into low, normal and high. For the interval (Rs. 10000 to Rs. 30000) we have normal salary, for (Rs. 10,000 and below) we have low salary and for (Rs.30, 000 and above )we have high salary. MH _.*. 2... kd fe 8 2 ad i nak nn ee, ee, ee
Fig 2:The two bad types of membership functions International Conference on Advances in Communication and Computing Technologies (ICACACT) 2012 Proceedings published by International Journal of Computer Applications® (IJCA)
Fig 2:The two bad types of membership functions International Conference on Advances in Communication and Computing Technologies (ICACACT) 2012 Proceedings published by International Journal of Computer Applications® (IJCA)
Table 3. The coverage and the overlap factor of ten chromosomes
Table 3. The coverage and the overlap factor of ten chromosomes
Besides, Kaya et al. also proposed several genetic-fuzzy data mining approaches to derive membership functions and fuzzy association rules. In [14] the proposed approach tries to derive membership functions, which can get a maximum profit within an interval of user specified minimum support values and then using the derived membership functions to mine fuzzy association rules. The concept of their idea is shown in Figure 3.
Besides, Kaya et al. also proposed several genetic-fuzzy data mining approaches to derive membership functions and fuzzy association rules. In [14] the proposed approach tries to derive membership functions, which can get a maximum profit within an interval of user specified minimum support values and then using the derived membership functions to mine fuzzy association rules. The concept of their idea is shown in Figure 3.
—— a ee As shown in Figure 4, it can be divided into two phases. The first phase searches for suitable minimum support values and membership functions of items and the second phase uses the final best set of minimum support values and membership functions to mine fuzzy association rules. The proposed framework maintains a population of sets of minimum support values and membership functions, and uses the genetic algorithm to automatically derive the resulting one. It first uses the k-means clustering approach to gather similar items into groups. All items in the same cluster are considered to have similar characteristics and are assigned similar values when a population is initialized. The values (or initialization information) include an appropriate number of linguistic terms for each item, its reasonable membership functions, and a range of its possible minimum support values. It then generates and encodes each set of minimum support values and membership functions into a fixed-length string according to the initialization information.
—— a ee As shown in Figure 4, it can be divided into two phases. The first phase searches for suitable minimum support values and membership functions of items and the second phase uses the final best set of minimum support values and membership functions to mine fuzzy association rules. The proposed framework maintains a population of sets of minimum support values and membership functions, and uses the genetic algorithm to automatically derive the resulting one. It first uses the k-means clustering approach to gather similar items into groups. All items in the same cluster are considered to have similar characteristics and are assigned similar values when a population is initialized. The values (or initialization information) include an appropriate number of linguistic terms for each item, its reasonable membership functions, and a range of its possible minimum support values. It then generates and encodes each set of minimum support values and membership functions into a fixed-length string according to the initialization information.
Table 4. The three clusters found from the ten chromosomes All the chromosomes in a cluster use the number of large 1- itemsets derived from the representative chromosome in the cluster and their own suitability of membership functions to calculate their fitness values. Since the number for scanning a database decreases, the evaluation cost can thus be reduced. In this example, the representative chromosomes are chromosomes C4, C5, C9 and it only needs to calculate the number of large 1-itemsets three times. The evaluation results are utilized to choose appropriate chromosomes for mating in the next generation. The offspring membership function sets then undergo recursive evolution until a good set of membership functions has been obtained. Finally, the derived membership functions are used to mine fuzzy association tules.
Table 4. The three clusters found from the ten chromosomes All the chromosomes in a cluster use the number of large 1- itemsets derived from the representative chromosome in the cluster and their own suitability of membership functions to calculate their fitness values. Since the number for scanning a database decreases, the evaluation cost can thus be reduced. In this example, the representative chromosomes are chromosomes C4, C5, C9 and it only needs to calculate the number of large 1-itemsets three times. The evaluation results are utilized to choose appropriate chromosomes for mating in the next generation. The offspring membership function sets then undergo recursive evolution until a good set of membership functions has been obtained. Finally, the derived membership functions are used to mine fuzzy association tules.
Related topics:
Computer ScienceFuzzy association rulesAssociation Rule Learning
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