Mining qualitative patterns in spatial cluster analysis

Authors:
Ickjai Lee;Yang Qu;Kyungmi Lee
Affiliations:
School of Business (IT), James Cook University, Cairns, QLD 4870, Australia;School of Business (IT), James Cook University, Cairns, QLD 4870, Australia;School of Business (IT), James Cook University, Cairns, QLD 4870, Australia
Venue:
Expert Systems with Applications: An International Journal
Year:
2012

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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.