A Hierarchical Projection Pursuit Clustering Algorithm

Authors:
Alexei D. Miasnikov;Jayson E. Rome;Robert M. Haralick
Affiliations:
The Graduate Center of The City University of New York;The Graduate Center of The City University of New York;The Graduate Center of The City University of New York
Venue:
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Year:
2004

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Abstract

We define a cluster to be characterized by regions of high density separated by regions that are sparse. By observing the downward closure property of density, the search for interesting structure in a high dimensional space can be reduced to a search for structure in lower dimensional subspaces. We present a Hierarchical Projection Pursuit Clustering (HPPC) algorithm that repeatedly bi-partitions the dataset based on the discovered properties of interesting 1-dimensional projections. We describe a projection search procedure and a projection pursuit index function based on Cho, Haralick and Yi's improvement of the Kittler and Illingworth optimal threshold technique. The output of the algorithm is a decision tree whose nodes store a projection and threshold and whose leaves represent the clusters (classes). Experiments with various real and synthetic datasets show the effectiveness of the approach.