Incremental Support Vector Clustering

Support vector clustering (SVC) is a flexible clustering method inspired by support vector machines (SVM). Due to its advantage in discovering clusters of arbitrary shapes, it has been widely used in many applications. However, one bottleneck which restricts the scalability of the method is its significantly high time complexity. Both of its two main stages, namely, sphere construction and cluster labeling, are quite time-consuming. Although some methods have been developed to speedup cluster labeling, it is still an intractable task to construct a sphere for a large-scale dataset. To this end, we propose a novel incremental support vector clustering (ISVC) algorithm, which constructs a sphere incrementally and efficiently. In our approach, by taking the data as arriving over time in chunks, the support vectors of the historical data and the data points of the new chunk are used to learn an updated sphere. Theoretical analysis has shown that the proposed ISVC algorithm can generate completely the same clustering results as SVC with much lower time and memory consumption. Experimental results on large-scale datasets have validated the theoretical analysis.