Intermediate Data Clustering with k-means

The k-means data clustering algorithm is extended to support missing data, mixed data, and to choose the number of clusters. A modified distance calculation finds solutions with a lower total error. Multi-dimensional scaling with missing data support is included. Full source code with no library dependencies is provided in FORTRAN with a full translation to C. Several example problems are discussed.