Discrimination through the Regularized Nearest Cluster Method
Ludovic Lebart · Computational Statistics · 1992
This paper contains three parts. The first part consists of a brief review of the discrimination techniques used when dealing with large arrays of sparse qualitative data. The second part presents the “Regularized Nearest Cluster Method”, an efficient and versatile technique of discrimination, well adapted to this kind of data. This technique is compared to some other existing methods likely to be used in similar contexts. The third part briefly discusses the interest of these methods in the domain of textual data analysis. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.