Abstract: Classification of class-imbalanced data becomes a research hot topic in machine learning and data mining．Most classification algorithms tend to predict that most of the incoming data belongs to the majority class,resulting in the pool classification performance in minority class instances,which are usually much more of interest．In this paper,a two-tier clustering cascading mining algorithm was proposed．The algorithm first constructs balanced training set by clusterd-based under-sampling,using K-means clustering to cluster majority class and extract cluster centroids then merge with all minority class instances to generate a balanced training set for training．To avoid the number of the minority is too small,leading the shortage of training instance,combination of SMOTE over-sampling and cluster-based under-sampling is used；next,using “K-means＋C4.5”,a method to cascade K-means clustering and C4.5decision tree algorithm for classifying on the balanced training set,the K-means clustering method is first used to parition the training instances into k clusters,and on each cluster,C4.5algorithm is used to build decision tree,the decision tree on each cluster refines the decision boundaries by learning the subgroups within the cluster．Experimental results show that the proposed method provides better classification performance than other approaches on both minority and majority classes,and is effective and feasible to deal with the imbalanced datasets．

Key words: Data mining,Classification,Imbalanced data,K-means clustering