Adaptive non-linear clustering in data streams

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 highdimensional and the clusters are not linearly separable in the input space. In this paper, we propose a non-linear 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 non-linearly onto a low-dimensional space (LDS), before assigning them to a cluster. We demonstrate the effectiveness o...

• Type: Conference
• Year: 2006
• Where: CIKM
• Authors: Ankur Jain, Zhihua Zhang, Edward Y. Chang
• Keywords: CIKM 2006, Data Stream, Data Stream Clustering, Information Management, Stream Clustering