关键词: 三维模型, 聚类分析, ART2网络, 聚类有效性评估, 数据挖掘

Abstract: Clustering results were always sensitive to dimensionality and input sequence of data in Three-Dimensional(3D) model clustering approaches. To solve this problem, Adaptive Resonance Theory(ART) based ART2 neural network was introduced to 3D model clustering. Adopting three indices which included Rand indice, adjustment Rand indice and mutual information indice as a reference point for judging clustering validity, the effect of ART2 networks five parameters：a, b, c, d and θ on clustering validation was evaluated through clustering experiments. Furthermore, a set of superior parameters was presented. On that basis, the effect of vigilance parameter on clustering result was analyzed qualitatively, which included the definite of maximum clustering number and clustering sensitivity to input sequence of data. The clustering experimental tests demonstrated the feasibility and effectiveness of ART2 network on model clustering.

Key words: three dimensional model, cluster analysis, adaptive resonance theory 2 network, clustering validity assessment, data mining