Clustering data streams in optimization and geography domains

Ling Yin Wei*, Wen-Chih Peng

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Scopus citations

Abstract

In this paper, we formulate a dual clustering problem in spatial data streams. A spatial data stream consists of data points with attributes in the optimization and geography domains. We aim at partitioning these objects into disjoint clusters such that at each time window (1) objects in the same cluster satisfy the transitively r-connected relation in the optimization and geography domains, and (2) the number of clusters is as minimal as possible. We propose a Hierarchical-Based Clustering algorithm (HBC). Specifically, objects are represented as a graph structure, called RGraph, where each node represents an object and edges indicate their similarity relationships. In light of RGraph, algorithm HBC iteratively merges clusters. Experimental results show the performance of the algorithm.

Original languageEnglish
Title of host publication13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009
Pages997-1005
Number of pages9
DOIs
StatePublished - 23 Jul 2009
Event13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009 - Bangkok, Thailand
Duration: 27 Apr 200930 Apr 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5476 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009
CountryThailand
CityBangkok
Period27/04/0930/04/09

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