OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Data mining: concepts and techniques
Data mining: concepts and techniques
On Bayesian model and variable selection using MCMC
Statistics and Computing
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Computational Statistics
Spatial neighborhood clustering based on data field
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
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Cluster analysis has long played an important role in a wide variety of data applications. When the clusters are irregular or intertwined, densitybased clustering is proved to be much more efficient. The quality of clustering result depends on an adequate choice of the parameters. However, without enough domain knowledge the parameter setting is somewhat limited in its operability. In this paper, a new method is proposed to automatically find out the optimal parameter value of the bandwidth. It is to infer the most suitable parameter value by the constructed model on parameter estimation. Based on the Bayesian Theorem, from which the most probability value for the bandwidth can be acquired in accordance with the inherent distribution characteristics of the original data set. Clusters can then be identified by the determined parameter values. The results of the experiment show that the proposed method has complementary advantages in the density-based clustering algorithm.