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电磁场理论概念在带约束聚类中的应用

Applying Electromagnetic Field Theory Concepts to Clustering with Constraints
课程网址: http://videolectures.net/ecmlpkdd09_vazirgiannis_aeftccc/  
主讲教师: Michalis Vazirgiannis
开课单位: 雅典欧洲银行
开课时间: 信息不详。欢迎您在右侧留言补充。
课程语种: 英语
中文简介:
这项工作表明了电磁场理论中的概念如何有效地用于有约束的聚类。该框架将矢量数据转换成一个完全连接的图形,或者直接处理给定的图形数据。用户约束由影响图形边缘权重的电磁场表示。然后将聚类算法应用于调整后的图形上,以K-不同的最短路径作为距离度量。我们的框架比MPCK方法、SS内核KMeans和KMeans+对角线度量提供了更好的准确性,即使使用很少的约束,也显著提高了其他方法无法成功分区的一些数据集的聚类性能,并且可以同时对向量和图数据集进行聚类。通过深入的实验评价,证明了这些优点。
课程简介: This work shows how concepts from the electromagnetic field theory can be efficiently used in clustering with constraints. The proposed framework transforms vector data into a fully connected graph, or just works straight on the given graph data. User constraints are represented by electromagnetic fields that affect the weight of the graph’s edges. A clustering algorithm is then applied on the adjusted graph, using k-distinct shortest paths as the distance measure. Our framework provides better accuracy compared to MPCK-Means, SS-Kernel-KMeans and KMeans+Diagonal Metric even when very few constraints are used, significantly improves clustering performance on some datasets that other methods fail to partition successfully, and can cluster both vector and graph datasets. All these advantages are demonstrated through thorough experimental evaluation.
关 键 词: 电磁场理论; 聚类算法; 对角度量
课程来源: 视频讲座网
最后编审: 2019-11-17:cwx
阅读次数: 32