An improved method for density-based clustering

Hong Jin, Shuliang Wang*, Qian Zhou, Ying Li

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)

Abstract

Knowledge discovery in large multimedia databases which usually contain large amounts of noise and high-dimensional feature vectors is an increasingly important research issue. Density-based clustering is proved to be much more efficient when dealing with such databases. However, its clustering quality mainly depends on the parameter setting. For the adequate choice of the parameters to be preset, it has difficulty in its operability without enough domain knowledge. To solve such problem, in this paper it proposed a new approach to immediately inference an appropriate value for one of the parameters named bandwidth. Based on the Bayesian Theorem, it is to infer the suitable parameter value by the constructed parameter estimation model. Then the user only has to preset the other parameter noise threshold. As a result, the clusters can be identified by the determined parameter values. The experimental results show that the proposed method has complementary advantages in the density-based clustering algorithm.

Original languageEnglish
Pages (from-to)347-368
Number of pages22
JournalInternational Journal of Data Mining, Modelling and Management
Volume6
Issue number4
DOIs
Publication statusPublished - 1 Jan 2014
Externally publishedYes

Keywords

  • Bayesian posterior probability estimation
  • DENCLUE
  • Density-based clustering
  • Optimal bandwidth selection

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Jin, H., Wang, S., Zhou, Q., & Li, Y. (2014). An improved method for density-based clustering. International Journal of Data Mining, Modelling and Management, 6(4), 347-368. https://doi.org/10.1504/IJDMMM.2014.066763