*K-Means and Cluster Models for Cancer Signatures
Biomolecular Detection and Quantification 13 (2017) 7-31
124 Pages Posted: 1 Feb 2017 Last revised: 5 Oct 2017
Date Written: January 30, 2017
We present *K-means clustering algorithm and source code by expanding statistical clustering methods applied in http://ssrn.com/abstract=2802753 to quantitative finance. *K-means is statistically deterministic without specifying initial centers, etc. We apply *K-means to extracting cancer signatures from genome data without using nonnegative matrix factorization (NMF). *K-means' computational cost is a fraction of NMF's. Using 1,389 published samples for 14 cancer types, we find that 3 cancers (liver cancer, lung cancer and renal cell carcinoma) stand out and do not have cluster-like structures. Two clusters have especially high within-cluster correlations with 11 other cancers indicating common underlying structures. Our approach opens a novel avenue for studying such structures. *K-means is universal and can be applied in other fields. We discuss some potential applications in quantitative finance.
Keywords: Clustering, K-Means, Nonnegative Matrix Factorization, Somatic Mutation, Cancer Signatures, Genome, Exome, DNA, eRank, Correlation, Covariance, Machine Learning, Sample, Matrix, Source Code, Quantitative Finance, Statistical Risk Model, Industry Classification, Bonds, Foreign Exchange, Alphas
JEL Classification: G00
Suggested Citation: Suggested Citation