Integrating Classification With K-Means to Detect E-Commerce Transaction Anomaly

Effective data mining solutions have been anticipated in Electronic Commerce (E-Commerce) transaction anomaly detection model to accurately predict anomaly transaction records. However, there are many sub-optimal E-Commerce transaction anomaly detection models due to highly imbalanced data set. This thesis proposes a meta-cluster with K-means algorithm to solve the problem of highly imbalanced data. This meta-cluster with K-means algorithm will be applied as a preprocessing method. The main aim is to generate a collection of clusters from the E-commerce transaction anomaly data set, each of which contains similar instances. The Logistic Regression, Naive Bayes, RBFNetwork and NBtree classifiers will be applied to evaluate the generated clusters. Results indicate that the proposed method can be easily realized and achieve excellent performance. The most important is that the proposed method can deal with the imbalanced data sets well and minimize type-II errors.