MID MINING: A LOGICAL COMBINATORIAL PATTERN RECOGNITION APPROACH TO CLUSTERING IN LARGE DATA SETS
Guillermo Sánchez-Díaz, José Ruíz-Shulcloper · 2000
In this paper, we expose the possibilities of the Logical Combinatorial Pattern Recognition tools for Data Mining from (Very) Large Mixed Incomplete Data Sets. Starting from the real existence of a lot of complex structured (very) large data sets, our Laboratories are working in the application of the methods, the techniques and in general, the philosophy of the Logical Combinatorial Pattern Recognition to the solution of supervised and unsupervised classification problems but with these kind of data sets. A clustering algorithm for determines all connected components from large structured mixed incomplete data set is commented and its performance and evaluation are included. 1.