A Hierarchical Projection Pursuit Clustering Algorithm

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.

• Type: Conference
• Year: 2004
• Where: ICPR
• Authors: Alexei D. Miasnikov, Jayson E. Rome, Robert M. Haralick
• Keywords: Computer Vision, Hierarchical Projection Pursuit, ICPR 2004, Projection Pursuit Index, Projection Search Procedure