Clustering by Random Projections

Abstract. Clustering algorithms for multidimensional numerical data must overcome special difficulties due to the irregularities of data distribution. We present a clustering algorithm for numerical data that combines ideas from random projection techniques and density-based clustering. The algorithm consists of two phases: the first phase that entails the use of random projections to detect clusters, and the second phase that consists of certain post-processing techniques of clusters obtained by several random projections. Experiments were performed on synthetic data consisting of randomly-generated points in Rn , synthetic images containing colored regions randomly distributed, and, finally, real images. Our results suggest the potential of our algorithm for image segmentation.

• Type: Conference
• Year: 2007
• Where: INCDM
• Authors: Thierry Urruty, Chabane Djeraba, Dan A. Simovici
• Keywords: Clustering Algorithm, INCDM 2007, Numerical Data, Random Projections