Adaptive non-linear clustering in data streams
Ankur Jain, Zhihua Zhang, Edward Yi Chang · 2006
Data stream clustering has emerged as a challenging and interesting problem over the past few years. Due to the evolving nature, and one-pass restriction imposed by the data stream model, traditional clustering algorithms are inapplicable for stream clustering. This problem becomes even more challenging when the data is high-dimensional and the clusters are not linearly separable in the input space. In this paper, we propose a nonlinear stream clustering algorithm that adapts to the stream's evolutionary changes. Using the kernel methods for dealing with the non-linearity of data separation, we propose a novel 2-tier stream clustering architecture. Tier-1 captures the temporal locality in the stream, by partitioning it into segments, using a kernel-based novelty detection approach. Tier-2 exploits this segment structure to continuously project the streaming data nonlinearly onto a low-dimensional space (LDS), before assigning them to a cluster. We demonstrate the effectiveness of our approach through extensive experimental evaluation on various real-world datasets.