Linear fuzzy clustering based on least absolute deviations
Katsuhiro Honda, N. Togo, Toju Fujii, H. Ichihashi · 2003
This paper proposes a technique of linear fuzzy clustering based on least absolute deviations. The novel method partitions a data set into several linear clusters by extracting local minor components. Using the least absolute deviations, the method provides robust clustering that is free from the influences of outliers.