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.