*K-means and Cluster Models for Cancer Signatures
Abstract
We present *K-means clustering algorithm and source code by expanding statistical clustering methods applied in https://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.
Cite
@article{arxiv.1703.00703,
title = {*K-means and Cluster Models for Cancer Signatures},
author = {Zura Kakushadze and Willie Yu},
journal= {arXiv preprint arXiv:1703.00703},
year = {2017}
}
Comments
124 pages, 69 figures; a trivial typo corrected; to appear in Biomolecular Detection and Quantification