We investigate how random projection can best be used for clustering high dimensional data. Random projection has been shown to have promising theoretical properties. In practice,...
Abstract. Clustering algorithms for multidimensional numerical data must overcome special difficulties due to the irregularities of data distribution. We present a clustering algo...
We present an algorithmic scheme for unsupervised cluster ensembles, based on randomized projections between metric spaces, by which a substantial dimensionality reduction is obtai...
This paper discusses the topic of dimensionality reduction for k-means clustering. We prove that any set of n points in d dimensions (rows in a matrix A ∈ Rn×d ) can be project...
We propose the use of random projections with a sparse matrix to maintain a sketch of a collection of high-dimensional data-streams that are updated asynchronously. This sketch al...
Aditya Krishna Menon, Gia Vinh Anh Pham, Sanjay Ch...