聚类分析中竞争学习的一种新算法
A NEW COMPETITIVE LEARNING ALGORITHM FOR CLUSTERING ANALYSIS
-
摘要: 分析指出RPCL算法的不足,提出一种竞争学习新算法。新算法引入数据点的密度定义,在权值的调整中考虑了数据集的几何结构对权值调整的影响,克服了RPCL算法的不足。理论分析与实验表明:新算法不仅可以自动确定数据集的类数,而且提高了聚类准确性和收敛速度。
-
关键词:
- 聚类分析; 竞争学习; 密度
Abstract: Based on the analysis of the defect of the RPCL, a new competitive learning algorithm is proposed. In the new algorithm the data density is introduced, and the modification of the weights is taken into account to surmount the defect of the RPCL. It is shown by the theoretical analysis and experimental results that the new algorithm can automatically select the appropriate number of the clusters in a data set, and improve the clustering accuracy and convergence speed. -
Rumelhart D E，Zipser D Feature discovery by competitive learning Cognitive Science，1985.9(1)：75-112．[2]Grossberg S．Competitive learning：from．Iterative activation to adaptive resonance. Cognitive Science，1987：11(1)：23-63．[3]Ahalt S C, Krishnamurty A K，Chen P，Meltion D E.Cmnpetitive learning algorithms for vector quantization[J].Neural Networks.1990,3(2):277-291[4]Xu L，Krzyzak A, Oja E．Riral penalized competitive learning for clustering analysis，RBF net，abd curve detection．IEEE Trans.on Neural Networks、1993：4(4)：636-649． -
计量
- 文章访问数: 2256
- HTML全文浏览量: 119
- PDF下载量: 491
- 被引次数: 0
下载:
