Unlabeled data classification via support vector machines and k -means clustering

Abstract

Support vector machines (SVMS), a powerful machine method developed from statistical learning and have made significant achievement in some field. Introduced in the early 90's, they led to an explosion of interest in machine learning. However, like most machine learning algorithms, they are generally applied using a selected training set classified in advance. With the repaid development of the Internet and telecommunication, huge of information has been produced as digital data format, generally the data is unlabeled. It is impossible to classify the data with one's own hand one by one in many realistic problems, so that the research on unlabeled data classification has been grown. Improvements in databases technology, computing performance and artificial intelligence have contributed to the development of intelligent data analysis. A SVM classifier based on k-means algorithm is presented for the classification of unlabeled data.