"K-means Clustering Using Random Matrix Sparsification," is the title of the IME Colloquium from 11 a.m.-noon tomorrow (Friday, April 6) in 211 Engineering Building. This presentation will focus on how an optimal k-means clustering solution of the sparse data matrix, obtained by applying random matrix sparsification, results in an approximately optimal k-means clustering objective of the original data matrix. Kaushik Sinha, assistant professor of Electrical Engineering and Computer Science Department, will provide empirical evaluations on three real world datasets which corroborate the theoretical findings and demonstrate that the proposed sparsification method can indeed achieve satisfactory clustering performance. Go to http://www.wichita.edu/j/?6365 for more information.
