Mean-Variance Analysis of the Performance of Spatial Clustering Methods
AbstractThis paper describes a simulation study investigating the performance of two non-recursive spatial clustering methods---the inverted naive and the spiral methods---in extensive detail and comparing them with the hilbert fractal method that has been shown in previous studies to outperform other recursive clustering methods. The paper highlights the importance of analyzing the sample variance when evaluating the relative performance of various spatial clustering methods. The clustering performance of the methods is examined in terms of both the mean and variance values of the number of clusters (runs of consecutive disk blocks) that must be accessed to retrieve a query region of a given size and orientation. The results show that, for a blocking factor of 1, the mean values for the spiral method are the best, and on average, about 30% better than for the other two methods. In terms of variance, the inverted naive method is the best followed by the spiral and hilbert methods, in that order. We also study the impact of varying query size and the skew ratio (between the X and Y dimensions) for each method. While these performance results do not generalize for higher blocking factors, we beleive that they are useful for both researchers and practitioners to know because several previous studies have also examined this special case, and also because it can arise in some important GIS applications as describe in the paper.
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Bibliographic InfoPaper provided by Ohio State University in its series Corporate Finance & Organizations with number _011.
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