An improved method for density-based clustering Online publication date: Wed, 21-Jan-2015
by Hong Jin; Shuliang Wang; Qian Zhou; Ying Li
International Journal of Data Mining, Modelling and Management (IJDMMM), Vol. 6, No. 4, 2014
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.
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