DistClusTree: A framework for distributed stream clustering

Zhinoos Razavi Hesabi, Timos Sellis, Kewen Liao

Research output: Chapter in Book/Report/Conference proceedingConference Paper published in Proceedings

Abstract

In this paper, we investigate the problem of clustering distributed multidimensional data streams. We devise a distributed clustering framework DistClusTree that extends the centralized ClusTree approach. The main difficulty in distributed clustering is balancing communication cost and clustering quality. We tackle this in DistClusTree through combining spatial index summaries and online tracking for efficient local and global incremental clustering. We demonstrate through extensive experiments the efficacy of the framework in terms of communication cost and approximate clustering quality.

Original languageEnglish
Title of host publicationDatabases Theory and Applications 29th Australasian Database Conference, ADC 2018, Proceedings
EditorsJunhu Wang, Gao Cong, Jinjun Chen, Jianzhong Qi
PublisherSpringer-Verlag London Ltd.
Pages288-299
Number of pages12
ISBN (Electronic)9783319920139
ISBN (Print)9783319920122
DOIs
Publication statusPublished - 26 Jun 2018
Externally publishedYes
Event29th Australasian Database Conference, ADC 2018 - Gold Coast, Australia
Duration: 24 May 201827 May 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10837 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference29th Australasian Database Conference, ADC 2018
CountryAustralia
CityGold Coast
Period24/05/1827/05/18

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