Abstract
Clustering is an important and challenging task in data mining. As a kind of generalized density-based clustering methods, DENCLUE algorithm has many remarkable properties, but the quality of clustering results strongly depends on the adequate choice of two parameters: density parameter σ and noise threshold ξ. In this paper, by investigating the influence of the two parameters of DENCLUE algorithm on the clustering results, we firstly show that an optimal σ should be chosen to obtain good clustering results. Then, an entropy-based method is proposed for the optimal choice of σ. Further, noise threshold ξ is estimated to produce a reasonable pattern of clustering. Finally, experiments are performed to illustrate the effectiveness of our methods.
Supported by the National Natural Science Foundation of China under Grant No. 69975024.
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© 2003 Springer-Verlag Berlin Heidelberg
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Gan, W., Li, D. (2003). Optimal Choice of Parameters for a Density-Based Clustering Algorithm. In: Wang, G., Liu, Q., Yao, Y., Skowron, A. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. RSFDGrC 2003. Lecture Notes in Computer Science(), vol 2639. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-39205-X_98
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DOI: https://doi.org/10.1007/3-540-39205-X_98
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