Abstract: Sparse Matrix Vector multiplication (SpMV) is a fundamental operation in various computational science applications, characterized by a significant degree of inherent parallelism. Recent ...
Abstract: Semi-supervised symmetric non-negative matrix factorization (SNMF) utilizes the available supervisory information (usually in the form of pairwise constraints) to improve the clustering ...
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This project implements Symmetric Non-negative Matrix Factorization (SymNMF) for clustering, combining Python and C for efficiency. It compares SymNMF with K-Means clustering on different datasets.
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