A framework for flexible clustering of multiple evolving data streams Online publication date: Thu, 30-Apr-2009
by Wei Fan, Toyohide Watanabe, Koichi Asakura
International Journal of Advanced Intelligence Paradigms (IJAIP), Vol. 1, No. 2, 2008
Abstract: In this paper, we propose a framework supporting clustering over different portions of continuous data streams at all possible time points. The framework is divided into two phases. Online statistics maintenance phase provides an approximation method for online statistics collection and a compact multi-resolution hierarchy for statistics maintenance. Once a clustering request is submitted, offline clustering phase abstracts statistics for approximating the user desired subsequences as precisely as possible from statistics hierarchies, and outputs the results of clustering over these statistics. Our performance experiments over real and synthetic data sets illustrate the effectiveness, efficiency of our approach.
Online publication date: Thu, 30-Apr-2009
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