An adaptive framework for clustering data streams

Abstract

In recent years, advances in hardware technology have facilitated the ability to collect data continuously. Simple transactions of everyday life such as using a credit card, a phone or browsing the web lead to automated data storage thus generating massive streams of data. These streams consist of millions or billions of updates and must be processed to extract the useful information to enable timely strategic decisions. Mining data streams have many inherent challenges among which the most important challenges are adapting to available resources and assuring quality of the output result. The purpose of this paper is to use a novel framework that accounts for both quality awareness and resource adaptation for clustering data streams. © 2011 Springer-Verlag.