An algorithm to cluster data for efficient classification of support vector machines

作者：Li Der Chiang^*; Fang Yao Hwei

来源：Expert Systems with Applications, 2008, 34(3): 2013-2018.

DOI：10.1016/j.eswa.2007.02.016

• density-based clustering algorithm
• machine learning
• support vector machines
• computational complexity

Support vector machines (SVM) are widely applied to various classification problems. However, most SVM need lengthy computation time when faced with a large and complicated dataset. This research develops a clustering algorithm for efficient learning. The method mainly categorizes data into clusters, and finds critical data in clusters as a substitute for the original data to reduce the computational complexity. The computational experiments presented in this paper show that the clustering algorithm significantly advances SVM learning efficiency.

• 出版日期2008-4