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<p><b>Abstract</b>—This paper presents a new approach to hierarchical clustering of point patterns. Two algorithms for hierarchical location- and density-based clustering are developed. Each method groups points such that maximum intracluster similarity and intercluster dissimilarity are achieved for point locations or point separations. Performance of the clustering methods is compared with four other methods. The approach is applied to a two-step texture analysis, where points represent centroid and average color of the regions in image segmentation.</p>
Point patterns, clustering, hierarchy of clusters, spatially interleaved clusters, density-based clustering, location-based clustering.

N. Ahuja and P. Bajcsy, "Location- and Density-Based Hierarchical Clustering Using Similarity Analysis," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 20, no. , pp. 1011-1015, 1998.
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