It is a challenging topic to develop an efficient algorithm for large scale classification problems in many applications of machine learning. In this paper, a hierarchical clustering and fixed-layer local learning (HCFLL) based support vector machine(SVM) algorithm is proposed to deal with this problem. Firstly, HCFLL hierarchically clusters a given dataset into a modified clustering feature tree based on the ideas of unsupervised clustering and supervised clustering. Then it locally trains SVM on each labeled subtree at a fixed-layer of the tree. The experimental results show that compared with the existing popular algorithms such as core vector machine and decision-tree support vector machine, HCFLL can significantly improve the training and testing speeds with comparable testing accuracy.
为了进一步提升ESSC聚类融合性能,采用实数值链接分析(real valued link analysis)计算聚类融合中模糊数据类的相似性。根据模糊决策及其相似性定义优化的融合信息,从而达到改进聚类性能的目的。实验选用了两个仿真数据库和五个UCI数据库。实验结果表明,基于实数值链接分析的ESSC聚类融合算法(RLA-ESSCE)的性能优于K-means聚类算法(KMC)、ESSC、ESSCE。
