Can I read Adaptive Non-linear Clustering in Data Streams on EtoBox?

Adaptive Non-linear Clustering in Data Streams by Ankur Jain; Zhihua Zhang; Edward Y. Chang is a scholarly article available to read on EtoBox.

What is Adaptive Non-linear Clustering in Data Streams about?

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 of our approach through extensive experimental evaluation on various real-world datasets.

Author

Ankur Jain; Zhihua Zhang; Edward Y. Chang

Publisher

ACM Press

Published

2006

Language

IT