Cluster Reduction Support Vector Machine for Large-Scale Data Set Classification

Support vector machine (SVM) has been a promising method for data mining and machine learning in recent years. However, the training complexity of SVM is highly dependent on the size of a data set. A cluster support vector machines (C-SVM) method for large-scale data set classification is presented to accelerate the training speed. By calculating cluster mirror radius ratio and representative sample selection in each cluster, the original training data set can be reduced remarkably without losing the classification information. The new method can provide an SVM with high quality samples in lower time consuming. Experiments with random data and UCI databases show that the C-SVM retains the high quality of training data set and the classification accuracy in data mining.