A Fast Randomized Clustering Method Based on a Hypothetical Potential Field

摘要：

A novel randomized clustering method is proposed to overcome some of the drawbacks of Mean Shift method. A hypothetical potential field is constructed from all the data points. Different from Mean Shift which moves the kernel window towards high-density region, our method moves the kernel window towards low-potential region. The proposed method is evaluated by comparing with both Mean Shift and K-means++ on three synthetic data sets which represent the clusters of different sizes, different shapes and different distributions. The experiments show that our method can produce more accurate results than both Mean Shift and K-means++.

关键词： Clustering potential field pattern recognition

作者: Yonggang Lu Li Liao Ruhai Wang

作者单位: School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu 730000,China Phillip M. Drayer Department of Electrical Engineering, Lamar University, Beaumont, TX 77710,USA

会议类型: 国际会议

会议名称: 第七届多光谱图象处理与模式识别国际学术会议

会议地点: 桂林

会议语种:英文

页码: 1-6

在线出版日期: 2011-11-01（万方平台首次上网日期，不代表论文的发表时间）