Improved Boundary Support Vector Clustering with Self-Adaption Support

Abstract

Concerning the good description of arbitrarily shaped clusters, collecting accurate support vectors (SVs) is critical yet resource-consuming for support vector clustering (SVC). Even though SVs can be extracted from the boundaries for efficiency, boundary patterns with too much noise and inappropriate parameter settings, such as the kernel width, also confuse the connectivity analysis. Thus, we propose an improved boundary SVC (IBSVC) with self-adaption support for reasonable boundaries and comfortable parameters. The first self-adaption is in the movable edge selection (MES). By introducing a divide-and-conquer strategy with the k-means++ support, it collects local, informative, and reasonable edges for the minimal hypersphere construction while rejecting pseudo-borders and outliers. Rather than the execution of model learning with repetitive training and evaluation, we fuse the second self-adaption with the flexible parameter selection (FPS) for direct model construction. FPS automatically selects the kernel width to meet a conformity constraint, which is defined by measuring the difference between the data description drawn by the model and the actual pattern. Finally, IBSVC adopts a convex decomposition-based strategy to finish cluster checking and labeling even though there is no prior knowledge of the cluster number. Theoretical analysis and experimental results confirm that IBSVC can discover clusters with high computational efficiency and applicability.

Selecting Indispensable Edge Patterns With Adaptive Sampling and Double Local Analysis for Data Description

“…Ping et al, 2015) and improved boundary SVC (IBSVC) (Y. Ping et al, 2022) are designed for direct model construction with edge patterns. Serving as the foundation for boundary SVC (BSVC) (Y.…”

“…For intuitive comparisons, the fourth series of experiments employs five data sets from Y. Ping et al (2022), which are listed in Table 1. Here, the breast cancer dataset wisconsin and shuttle data are provided by UCI repository (Frank & Asuncion, 2010).…”

“…Utilizing the edge patterns collected by IEPS as input, both the fast and scalable support vector clustering (FSSVC) (Y. Ping et al 2015) and the improved boundary support vector clustering (IBSVC) (Li et al 2022) perform well in terms of accuracy.…”