In this research work we use rule induction in data mining to obtain the accurate results with fast processing time. We using decision list induction algorithm to make order and unordered list of rules to coverage of maximum data from the data set. Using induction rule via association rule mining we can generate number of rules for training dataset to achieve accurate result with less error rate. We also use induction rule algorithms like confidence static and Shannon entropy to obtain the high rate of accurate results from the large dataset. This can also improves the traditional algorithms with good result.
As the competition grows in the market, the organizations are more concern about customers than products. To be in the competition, organizations always want to retain their profitable customers. To predict the customers" churn behavior, data mining techniques are used. Many algorithms have been proposed to predict churning customers. In this paper, enhanced boosted trees technique is used for customer churn prediction. This approach will improve the performance of existing boosted trees technique. The process of customer churn prediction is discussed in this paper. It will be used to identify churn customers and effective marketing strategies could be planned for this group of customers.
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