This Article 
 Bibliographic References 
 Add to: 
Location- and Density-Based Hierarchical Clustering Using Similarity Analysis
September 1998 (vol. 20 no. 9)
pp. 1011-1015

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.

[1] C.T. Zahn, "Graph-Theoretical Methods for Detecting and Describing Gestalt Clusters," IEEE Trans. Computers, vol. 20, pp. 68-86, Jan. 1971.
[2] D. Blostein and N. Ahuja, “Shape From Texture: Integrating Texture-Element Extraction and Surface Estimation,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 11, no. 12, pp. 1233-1251, Dec. 1989.
[3] A.K. Jain and R.C. Dubes, Algorithms for Clustering Data.Englewood Cliffs, NJ: Prentice Hall, 1988.
[4] R.O. Duda and P.E. Hart, Pattern Classification and Scene Analysis.New York, NY: Wiley, 1973.
[5] B.S. Everitt, Cluster Analysis.London: Edward Ar nold, 1993.
[6] N. Ahuja and B.J. Schachter, Pattern Models.New York, NY: John Wiley, 1983.
[7] K.C. Gowda and G. Krishna, "Agglomerative Clustering Using the Concept of Mutual Nearest Neighborhood," Pattern Recognition, vol. 10, pp. 105-112, 1978.
[8] M. Nadler and E. Smith, Pattern Recognition Engineering.Canada: John Wiley, 1993.
[9] P.H. Sneath and R.R. Sokal, Numerical Taxonomy.San Francisco, CA: W.H. Freeman, 1973.
[10] H. Hanaizumi, S. Chino, and S. Fujimura, "A Binary Division Algorithm for Clustering Remotely Sensed Multispectral Images," IEEE Trans. Instrumentation and Measurement, vol. 44, pp. 759-763, June 1995.
[11] Y. Wong and E.C. Posner, "A New Clustering Algorithm Applicable to Multiscale and Polarimetric SAR Images," IEEE Trans. Geoscience and Remote Sensing, vol. 31, pp. 634-644, May 1993.
[12] M. Tüceryan and A.K. Jain,“Texture segmentation using Voronoi polygons,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 12, no. 2, pp. 211-216, Feb. 1990.
[13] P. Bajcsy and N. Ahuja, "Uniformity and Homogeneity Based Hierarchical Clustering," Proc. 13th Int'l Conf. Pattern Recognition, vol. B, pp. 96-100,Vienna, Austria, 1996.
[14] M.G. Kendall, The Advanced Theory of Statistics, vol. 2. New York, NY: Hafner, 1951.
[15] A.S. Fotheringham and F.B. Zhan, "A Comparison of Three Exploratory Methods for Cluster Detection in Spatial Point Patterns," Geographical Analysis, vol. 28, pp. 200-218, July 1996.
[16] A. Getis and B. Boots, Models of Spatial Process: An Approach to the Study of Point, Line, and Area Patterns.London: Cambridge Univ. Press, 1978.

Index Terms:
Point patterns, clustering, hierarchy of clusters, spatially interleaved clusters, density-based clustering, location-based clustering.
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, Sept. 1998, doi:10.1109/34.713365
Usage of this product signifies your acceptance of the Terms of Use.