Unsupervised Clustering In Streaming Data

Tools for automatically clustering streaming data are becoming increasingly important as data acquisition technology continues to advance. In this paper we present an extension of conventional kernel density clustering to a spatio-temporal setting, and also develop a novel algorithmic scheme for clustering data streams. Experimental results demonstrate both the high efficiency and other benefits of this new approach.

• Type: Conference
• Year: 2006
• Where: ICDM
• Authors: Dimitris K. Tasoulis, Niall M. Adams, David J. Hand
• Keywords: Conventional Kernel Density, Data Acquisition Technology, Data Mining, ICDM 2006, Streaming Data