Cluster validity through graph-based boundary analysis
Jianhua Yang, Ickjai Lee · ResearchOnline at James Cook University (James Cook University) · 2004
Abstract- Gaining confidence that a clustering algorithm has produced meaningful results and not an accident of its usually heuristic optimization is central to data mining. This is the issue of cluster validity. We propose here a method by which proximity graphs are used to effectively detect border points and measure the margin between clusters. With analysis of boundary situation, we design a framework and relevant working principles to evaluate the separation and compactness in the clustering results. The method can obtain an insight into the internal structure in clustering result.