Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Building Data Mining Applications for CRM
Building Data Mining Applications for CRM
Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Postprocessing Decision Trees to Extract Actionable Knowledge
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Using AUC and Accuracy in Evaluating Learning Algorithms
IEEE Transactions on Knowledge and Data Engineering
Pattern Recognition Using Neural and Functional Networks
Pattern Recognition Using Neural and Functional Networks
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Data mining techniques to customer relationship management (CRM), is that there is a lack of a comprehensive literature review and a classification scheme for it. This paper indicate that the research area of customer retention received most research attention. Classification and Association models are the two commonly used models for data mining in CRM. Algorithms are used in data mining to discover customer models for distribution information, that are used in Customer Relationship Management (CRM), to point out customers who are loyal and who are attritors, but human experts is a must for discovering knowledge manually. Many post processing techniques have been introduced that do not suggest action to increase the objective function such as profit. In this paper, a feature extraction technique is proposed for the best approximation property and to increase the profit. To deal with high dimensional data, efficient and robust feature extraction and feature selection methods are necessary. Feature extraction can be achieved by implementing some criteria from which an optimal solution can be found. An automatic non-parameter Uncorrelated Discriminant Analysis (UDA) algorithm based on Maximum Margin Criterion (MMC) is used. The extracted features via UDA are statistically uncorrelated. It serves as an effective solution for small sample size problem. The extracted features are given as an input to a novel algorithm that suggests actions to change the customer from the undesired status to the desired one. These algorithms can discover the reduction in cost and transform customer from undesirable classes to desirable ones. The UDA algorithm is evaluated in terms of classification accuracy and robustness. Many tests have been conducted and experimental results have been analyzed in this paper