An Efficient Data Preprocessing Procedure for Support Vector Clustering
Jeen-Shing Wang (National Cheng Kung University, Taiwan, R.O.C.)
Jen-Chieh Chiang (National Cheng Kung University, Taiwan, R.O.C.)
Abstract: This paper presents an efficient data preprocessing procedure for the of support vector clustering (SVC) to reduce the size of a training dataset. Solving the optimization problem and labeling the data points with cluster labels are time-consuming in the SVC training procedure. This makes using SVC to process large datasets inefficient. We proposed a data preprocessing procedure to solve the problem. The procedure contains a shared nearest neighbor (SNN) algorithm, and utilizes the concept of unit vectors for eliminating insignificant data points from the dataset. Computer simulations have been conducted on artificial and benchmark datasets to demonstrate the effectiveness of the proposed method.
Keywords: noise elimination, shared nearest neighbors, support vector clustering
Categories: I.2.6, I.5.0, I.5.1, I.5.3