 An improved method for density-based clusteringHong Jin, Shuliang Wang, Qian Zhou and Ying Li International Journal of Data Mining, Modelling and Management, 2014, vol. 6, issue 4, 347-368 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. Keywords: density-based clustering; DENCLUE; optimal bandwidth selection; Bayesian posterior probability estimation; knowledge discovery; multimedia databases; parameter estimation. (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:ids:ijdmmm:v:6:y:2014:i:4:p:347-368 Access Statistics for this article More articles in International Journal of Data Mining, Modelling and Management from Inderscience Enterprises Ltd |
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