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| Peter Bajcsy, Narendra Ahuja, "Location- and Density-Based Hierarchical Clustering Using Similarity Analysis," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. 9, pp. 1011-1015, September, 1998. | |||
| BibTex | x | ||
| @article{ 10.1109/34.713365, author = {Peter Bajcsy and Narendra Ahuja}, title = {Location- and Density-Based Hierarchical Clustering Using Similarity Analysis}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {20}, number = {9}, issn = {0162-8828}, year = {1998}, pages = {1011-1015}, doi = {http://doi.ieeecomputersociety.org/10.1109/34.713365}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - JOUR JO - IEEE Transactions on Pattern Analysis and Machine Intelligence TI - Location- and Density-Based Hierarchical Clustering Using Similarity Analysis IS - 9 SN - 0162-8828 SP1011 EP1015 EPD - 1011-1015 A1 - Peter Bajcsy, A1 - Narendra Ahuja, PY - 1998 KW - Point patterns KW - clustering KW - hierarchy of clusters KW - spatially interleaved clusters KW - density-based clustering KW - location-based clustering. VL - 20 JA - IEEE Transactions on Pattern Analysis and Machine Intelligence ER - | |||
Abstract—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.
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