An autonomous method on tracking stream data cluster evolution

Stream data can often show abrupt changes over time. It is very critical to analyze and predict the trends autonomously on time in the continuous, high speed and variable stream data environment. In this paper, we discuss the Autonomous Real-time Clustering Evolution Tracking algorithm which integrate the fractal cluster technique, self-adaptive sampling technique with the restriction of computing resource and the requirement of processing speed, and can discriminate the cluster evolution of stream data on time autonomously. Our performance experiments over a number of real and synthetic data sets illustrate the effectiveness and efficiency provided by our approach.