On pre-processing algorithms for data stream

Abstract

Clustering is a one of the most important tasks of data mining. Algorithms like the Fuzzy C-Means and Possibilistic C-Means provide good result both for the static data and data streams. All clustering algorithms compute centers from chunk of data, what requires a lot of time. If the rate of incoming data is faster than speed of algorithm, part of data will be lost. To prevent such situation, some pre-processing algorithms should be used. The purpose of this paper is to propose a pre-processing method for clustering algorithms. Experimental results show that proposed method is appropriate to handle noisy data and can accelerate processing time. © 2012 Springer-Verlag Berlin Heidelberg.