关键词: 密度聚类, SVM算法, 快速分类, 大数据集

Abstract: In order to resolve the problem of actual large-scale data sets classification using SVM，this paper provides a method to improve the training speed through reducing data sets.The algorithm is divided into two steps.Firstly，it finds the samples that can represent a similar regional utilizing density clustering，then a set of support vectors will be gotten after using SVM to train reduced data sets.The second step is to find a new train data set assembled some support vectors and samples which belong to the regional represented by the support vector.The simulation shows that the algorithm proposed in this paper can improve classification speed while accuracy rate is acceptable.

Key words: density clustering, Support Vector Machine（SVM） algorithm, fast classification, large data sets