  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.
