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Ester, M., Kriegel, H.P., Sander, J. and Xu, X. (1996) A Density Based Algorithm for Discover Clusters in Large Spatial Datasets with Noise. Proceedings of International Conference on Knowledge Discovery and Data Mining, 226-231.

has been cited by the following article:

• TITLE: Improved Clustering Algorithm Based on Density-Isoline

  AUTHORS: Bin Yan, Guangming Deng

  KEYWORDS: Density-Isolines, Density-Based Clustering, Clustering Algorithm, Noise

  JOURNAL NAME: Open Journal of Statistics, Vol.5 No.4, June 10, 2015

  ABSTRACT: An improved clustering algorithm was presented based on density-isoline clustering algorithm. The new algorithm can do a better job than density-isoline clustering when dealing with noise, not having to literately calculate the cluster centers for the samples batching into clusters instead of one by one. After repeated experiments, the results demonstrate that the improved density-isoline clustering algorithm is significantly more efficiency in clustering with noises and overcomes the drawbacks that traditional algorithm DILC deals with noise and that the efficiency of running time is improved greatly.