Incremental support vector clustering with outlier detection

Dong Huang, Jian-Huang Lai, Chang-Dong Wang

Abstract: Support vector clustering (SVC) is a nonparametric clustering algorithm inspired by support vector machines. Incremental support vector clustering (ISVC) extends the SVC algorithm to an incremental version for the case of large-scale datasets with the assumption of no outliers. In order to tackle the problem of clustering large-scale noisy datasets, this paper proposes the algorithm termed incremental support vector clustering with outlier detection (OD-ISVC). The proposed algorithm consists of two components, namely, incremental support vector (SV) construction and dynamic bounded support vector (BSV) management. We introduce the concept of BSV-pool, where the check and recycle procedure is designed for updating the temporarily stored BSVs and detecting outliers. The experiments on real and synthetic datasets demonstrate the effectiveness and efficiency of our method.