A framework for flexible clustering of multiple evolving data streams. (1st May 2009)

• Record Type: Journal Article
• Authors: Fan, Wei; Watanabe, Toyohide; Asakura, Koichi
• Is Part Of: International journal of advanced intelligence paradigms. Volume 1:Number 2(2008)
• Page Start: 178
• Page End: 195
• Publication Date: 2009-05-01
• Subjects: data stream mining -- flexible clustering -- multiple data streams -- one data scan -- summarisation statistics hierarchy -- adaptive abstraction -- subsequences; Artificial intelligence -- Periodicals; Machine theory -- Periodicals; Fuzzy logic -- Periodicals; 006.305
• Languages: English
• ISSNs: 1755-0386

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.