pcStream: A stream clustering algorithm for dynamically detecting and managing temporal contexts

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

15 Scopus citations

Abstract

The clustering of unbounded data-streams is a difficult problem since the observed instances cannot be stored for future clustering decisions. Moreover, the probability distribution of streams tends to change over time, making it challenging to differentiate between a concept-drift and an anomaly. Although many excellent data-stream clustering algorithms have been proposed in the past, they are not suitable for capturing the temporal contexts of an entity. In this paper, we propose pcStream; a novel data-stream clustering algorithm for dynamically detecting and managing sequential temporal contexts. pcStream takes into account the properties of sensor-fused data-streams in order to accurately infer the present concept, and dynamically detect new contexts as they occur. Moreover, the algorithm is capable of detecting point anomalies and can operate with high velocity data-streams. Lastly, we show in our evaluation that pcStream outperforms state-of-the-art stream clustering algorithms in detecting real world contexts from sensor-fused datasets. We also show how pcStream can be used as an analysis tool for contextual sensor streams.

Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 19th Pacific-Asia Conference, PAKDD 2015, Proceedings
EditorsTru Cao, Ee-Peng Lim, Tu-Bao Ho, Zhi-Hua Zhou, Hiroshi Motoda, David Cheung
PublisherSpringer Verlag
Pages119-133
Number of pages15
ISBN (Electronic)978-3-319-18032-8
ISBN (Print)9783319180311
DOIs
StatePublished - 9 May 2015
Event19th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2015 - Ho Chi Minh City, Viet Nam
Duration: 19 May 201522 May 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9078
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference19th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2015
Country/TerritoryViet Nam
CityHo Chi Minh City
Period19/05/1522/05/15

Keywords

  • Concept detection
  • Concept drift
  • Context-awareness
  • Stream clustering

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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