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An Enhanced Artificial bee Colony (EABC) Process Model for CRM using Data Mining Techniques in Banking Institutions

Rekha Arun, Dr. J. Jebamalar Tamilselvi


Today, banks are facing a forceful competition and they have to attempt endeavors to get by an aggressive and uncertain marketplace. Banks have understood that overseeing customer relationships are an imperative variable for their prosperity. The principle issue is that the huge measure of information must be transformed into helpful data in the well-suited way, keeping in mind the end goal to contribute the administrator with solid data for their choice. Leveraging the power of increasing amounts of data to analyze customer base for attracting and retaining the most valuable customers is a major problem facing companies in this information age. Data mining technologies extract hidden information and knowledge from large data stored in databases or data warehouses, thereby supporting the corporate decision making process. CRM uses data mining (one of the elements of CRM) techniques to interact with customers. This study investigates the use of a technique, semi-supervised learning, for the management and analysis of customer-related data warehouse and information. There are many metaheuristic approaches which are widely used like Genetic algorithm (GA), Ant colony optimization (ACO), Artificial bee colony (ABC) algorithms. In this paper, Enhanced Artificial bee colonies (EABC) are proposed to predict the category of an unknown customer (potential customers). In addition, this technique can be used with Rapid Miner tools for both labeled and unlabeled data.

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