A FCM Clustering Algorithm Based on Semi-supervised and Point Density Weighted

摘要：

The effect of FCM depends on the samples distribution. The optimum clustering result might be not valid for the data sets having mass shape and large discrepancy of every class specimen number. Therefore, a Semisupervised and Point Density Weighted Fuzzy C-means clustering (SSWFCM) is proposed. This algorithm using distancebased semi-supervised learning studies the training data set and gets coefficient matrix of each category, and then using the distance formula with a coefficient and point density weighted clusters the test data sets. The experiment proves that SSWFCM is superior to FCM in the clustering accuracy and validity. Moreover, the introduction of point density weight making SSWFCM can handle data sets with different distributions.

关键词： Fuzzy C-Means Clustering Semi-supervised Point Density Weighted

作者: Xiaobin Zhang Hui Huang Shijing Zhang

作者单位: School of Computer Science Xian Polytechnic University Xian, China

会议类型: 国际会议

会议名称: 2010 IEEE International Conference on Intelligent Computing and Intelligent Systems(2010 IEEE 智能计算与智能系统国际会议 ICIS 2010)

会议地点: 厦门

会议语种:英文

页码: 710-713

在线出版日期: 2010-10-29（万方平台首次上网日期，不代表论文的发表时间）