%0 Journal Article
%T 2D Mapping of Large Quantities of Multi-variate Data
%A £¿upan
%A Jure
%J -
%D 2002
%X Sa£¿etak A new method for £¿intelligent£¿ or £¿content dependent£¿ retrieval of objects from among a large quantities of multi-variate data is devised and explained. The method is based on the combination of two different approaches. One is the multi-branching decision tree and the second is Kohonen neural network. The new method allows a retrieval of similar or identical objects from a number of N objects (N being in the order of 106 and more) in a number of comparisons proportional to log9N. The method was developed in the connection with the question £¿how to map millions of multi-dimensional objects like spectra, structures, time-series of process variables, multi-component analyses of food or pharmaceutical Products, etc.?£¿. In order to show how the proposed method works, a small example of 572 objects (8-component analyses of various olive oils) is described
%K artificial neural networks
%K Kohonen learning
%K binary decision trees
%K clustering
%K large databases
%K Chemical analysis
%K algorithms
%U https://hrcak.srce.hr/index.php?show=clanak&id_clanak_jezik=188307