Applying Electromagnetic Field Theory Concepts To Clustering With Constraints
MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES, PT I(2009)
摘要
This work shows how concepts from the electromagnetic field theory can be efficiently used ill 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.
更多查看译文
关键词
Data Clustering,User Constraints,Electromagnetic Field Theory
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要