Novel Support Vector Clustering with Label Assignment in Enriched Neighborhood

Support vector clustering (SVC) is an appealing approach that can detect cluster boundaries. In spite of its popularization in applications, it sees the critical bottleneck in cluster labeling. This paper presents a novel support vector clustering algorithm (NSVC) to go a further step in clustering labeling. NSVC consists of three phases: extract data representatives (DRs); cluster DRs; label non-DR data. The objective of traditional SVC is used by NSVC for finding DRs, but the kernel scale of the objective is modified. DRs are grouped by spectrum analysis (SA) method, which simultaneously develops an informative metric. Non-DR data are labeled by a weighted kNN procedure that works in query's neighborhood, which is formulated with the new metric and then enriched by the convex hull skill. Experiments on real datasets demonstrate the improvement of NSVC over its peers and the competitive performance with the state of the arts.