Improved density based algorithm for data stream clustering

Maryam Mousavi, Azuraliza Abu Bakar

Research output: Contribution to journalArticle

Abstract

In recent years, clustering methods have attracted more attention in analysing and monitoring data streams. Density-based techniques are the remarkable category of clustering techniques that are able to detect the clusters with arbitrary shapes and noises. However, finding the clusters with local density varieties is a difficult task. For handling this problem, in this paper, a new density-based clustering algorithm for data streams is proposed. This algorithm can improve the offline phase of density-based algorithm based on MinPts parameter. The experimental results show that the proposed technique can improve the clustering quality in data streams with different densities.

Original languageEnglish
Pages (from-to)73-77
Number of pages5
JournalJurnal Teknologi
Volume77
Issue number18
DOIs
Publication statusPublished - 2015

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Clustering algorithms
Monitoring

Keywords

  • Data streams
  • Density-based clustering

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Improved density based algorithm for data stream clustering. / Mousavi, Maryam; Abu Bakar, Azuraliza.

In: Jurnal Teknologi, Vol. 77, No. 18, 2015, p. 73-77.

Research output: Contribution to journalArticle

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