@inproceedings{b690ec6d550e4b62b2c44bbf59dbf9a4,
title = "Uniformity and homogeneity-based hierarchical clustering",
abstract = "This paper presents a clustering algorithm for dot patterns in n-dimensional space. The n-dimensional space often represents a multivariate (nf-dimensional) function in a ns-dimensional space (ns+nf=n). The proposed algorithm decomposes the clustering problem into the two lower dimensional problems. Clustering in n f-dimensional space is performed to detect the sets of dots in n-dimensional space having similar nf-variate function values (location based clustering using a homogeneity model). Clustering in n s dimensional space is performed to detect the sets of dots in n-dimensional space having similar interneighbor distances (density based clustering with a uniformity model). Clusters in the n-dimensional space are obtained by combining the results in the two subspaces.",
author = "Peter Bajcsy and Narendra Ahuja",
year = "1996",
month = jan,
day = "1",
doi = "10.1109/ICPR.1996.546731",
language = "English (US)",
isbn = "081867282X",
series = "Proceedings - International Conference on Pattern Recognition",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "96--100",
booktitle = "Track B",
address = "United States",
note = "13th International Conference on Pattern Recognition, ICPR 1996 ; Conference date: 25-08-1996 Through 29-08-1996",
}