A Normalized Criterion of Spatial Clustering in Model-Based Framework

This paper presents a model-based criterion for assessing the clustering results of spatial data, where both geometrical constraints and observation attributes are taken into account. An extra parameter is often used in the aim of controlling the importance of each characteristic. Since the values of both terms vary according to different realizations of data, it becomes essential to determine the parameter value which has a large influence on the clustering criterion value. Thus, an `upper-lower bound´ technique is proposed to solve that problem caused by stochastic properties in both terms. In addition, we apply a normalization method to regularize the parameter value. The effectiveness of this approach is validated through the experimental results by using simulated reliability data.