Robust Clustering Methods For Incomplete AndErroneous Data

In this paper, reliable methods for clustering erroneous and incomplete data per se (e.g. without imputation) are considered. For this purpose, the usual K-means algorithm is generalized by using robust location estimates and special projection technique. Numerical comparison of the resulting methods with simulated data are presented and analyzed.