Mining qualitative patterns in spatial cluster analysis

Abstract

Clustering is an important concept formation process within AI. It detects a set of objects with similar characteristics. These similar aggregated objects represent interesting concepts and categories. As clustering becomes more mature, post-clustering activities that reason about clusters need a great attention. Numerical quantitative information about clusters is not as intuitive as qualitative one for human analysis, and there is a great demand for an intelligent qualitative cluster reasoning technique in data-rich environments. This article introduces a qualitative cluster reasoning framework that reasons about clusters. Experimental results demonstrate that our proposed qualitative cluster reasoning reveals interesting cluster structures and rich cluster relations.