Clustering with r-regular graphs
Pattern Recognition
Noise-enhanced clustering and competitive learning algorithms
Neural Networks
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Clustering is a discovery process in data mining an can be used to group together the objects of a database into meaningful subclasses which serve as the foundation for other data analysis techniques.In this paper, we focus on dealing with a set of spatial data. For the spatial data, the clustering problem becomes that of finding the densely populate regions of the space and thus grouping these regions into clusters such that the intracluster similarity is maximized and theintercluster similarity is minimized. We develop a novel hierarchical clustering algorithm that uses a hypergraph to represent a set of spatial data. This hypergraph is initially constructed from the Delaunay triangulation graph of the data set and can correctly capture the relationships among sets of data points. Two phases are developed for the proposed clustering algorithm to find the clusters in the data set.We evaluate our hierarchical clustering algorithm with some spatial data sets in which contain clusters of different sizes, shapes, densities, and noise. Experimental results on these data sets are very encouraging.