期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2014
卷号：66
期号：3
出版社：Journal of Theoretical and Applied
摘要：Identifying customers which are more likely potential for a product and service offering is an important issue. In customers identification data mining has been used extensively to predict potential customers for a product and service. Modern companies and organizations efficiently implement a CRM strategy for managing a company interactions and relationships with customers. CRM systems have been developed and designed to support the areas of marketing, service process and sales. Many literature studies are available to preserve the customer relationship but small drawbacks occur in the existing methods. One method to maintain the customer relationship is frequency based method i.e., The Company will give declination to the customer based on the historical data that is the customers how many times come to that company. These methods are not effective. Because the customers give revenue to that company is less. So the company revenue is affected. In the data mining field, association rules have been researched for more than ten years ; however, the degree to which the support threshold effectively discovers interesting association rules has received little attention. Most of the research effort in the scope of association rules has been oriented to simplify the rule set and to improve performance of the algorithm. With the recent advancement of Internet and Web Technology, web search has taken an important role in the ordinary life. To discover interesting patterns or relationship between data in large database association rule mining is used. Association rule mining can be an important data analysis method to discover associate rules in CRM. The Apriori algorithm is a proficient algorithm for determining all frequent customers in CRM. But these are not the only problems that can be found and when rules are generated and applied in different domains. Troubleshooting for them should also take into consideration the purpose of association model and the data they come from. Some of drawbacks like non interesting rules, low algorithm performance arts are found in the algorithm. Several past studies addressed the problem of mining association rules with different Supports will not be appropriate in large dataset and they cannot generate more useful rules. This paper suggests a new framework of algorithm MAA that overcomes the limitations associated with existing methods and enables the finding of association rules based on Apriori Algorithm among the presence and/or absence of a set of items without a preset minimum support threshold and Minimizing Candidate Generation. The proposed work is an efficient algorithm for generating frequent itemsets and is optimized to takes less time compare to the existing algorithms. The main aim of this algorithm is to reduce execution time and memory utilization as compared to the existing algorithms. The framework has been tested on several datasets.. The result obtained shows that the proposed algorithm takes 25% less time compared to the Apriori algorithm in all instances. The performance of the algorithm is influenced by the dimensions of the data set and support factor and it is compared with performance of FP-growth and DynFP-Growth algorithms. The algorithm used to discover coherent rules which has been used in CRM model .Then, the mined information is used to calculate the company profit and frequency (the number of times the particular customer visit the company). By using association rule mining, the profit and frequency value of each customer is computed. Based on the mining result, the companies provide offers to customer using swarm intelligence technique known as particle swarm optimization.. This offer does not affect the company revenues as well as satisfying the customers. This process will make a good relationship between the customers and organizations and to satisfy the customers forever with company�s rules.
关键词：CRM; PSO; Data Mining; Association Rule Mining; Algorithm; DynFP-Growth
