Machine Learning
Customer Relationship Management: Getting It Right!
Customer Relationship Management: Getting It Right!
Additive Groves of Regression Trees
ECML '07 Proceedings of the 18th European conference on Machine Learning
Expert Systems with Applications: An International Journal
Identity disclosure protection: A data reconstruction approach for privacy-preserving data mining
Decision Support Systems
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Privacy-preserving data mining: A feature set partitioning approach
Information Sciences: an International Journal
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Effective data mining solutions have for long been anticipated in Customer Relationship Management (CRM) to accurately predict customer behavior, but in a lot of research works we have observed sub-optimal CRM classification models due to inferior data quality inherent to CRM data set. This paper is proposed to present our new classification framework, termed Partial Focus Feature Reduction, poised to resolve CRM data set with Reduced Dimensionality using a collection of efficient data preprocessing techniques characterizing a specially tailored modality grouping method to significantly improve feature relevancy as well as reducing the cardinality of the features to reduce computational cost. The resulting model yields very good performance result on a large complicated real-world CRM data set that is much better than ones from complex models developed by renowned data mining practitioners despite all data anomalies.