This paper presents an efficient data preprocessing procedure for the support of 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.
|Number of pages||17|
|Journal||Journal of Universal Computer Science|
|Publication status||Published - 2009 Jul 15|
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)