Agglomerative Hierarchical Clustering based on Affinity Propagation Algorithm

摘要：

Affinity propagation (AP) algorithm doesnt fix the number of the dusters and doesnt rely on random sampling. It exhibits fast execution speed with low error rate. However, it is hard to generate optimal clusters. This paper proposes an agglomerative clustering based on AP (agAP) method to overwhelm the limitation. It puts forward k-cluster closeness to merge the clusters yielded by AP. In comparison to AP, agAP method has better performance and is better than or equal to the quality of AP method. And it has an advantage of time complexity compared to adaptive affinity propagation (adAP).

关键词： Affinity propagation. Cluster closeness Agglomerative hierarchical clustering based on A Adaptive Affinity Propagation

作者: Qinghe Zhang Xiaoyun Chen

作者单位: College of Mathematics and Computer Science University ofFuzhou Fuzhou 350108, Fujian Province, China

会议类型: 国际会议

会议名称: 2010 Third International Symposium on Knowledge Acquisition and Modeling(第三届知识获取与建模国际研讨会 KAN 2010)

会议地点: 武汉

会议语种:英文

页码: 250-253

在线出版日期: 2010-10-20（万方平台首次上网日期，不代表论文的发表时间）