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2D Mapping of Large Quantities of Multi-variate Data

Jure Župan ; National Institute of Chemistry, Hajdrihova 19, 1000 Ljubljana, Slovenia

Sažetak

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.

Ključne riječi

artificial neural networks; Kohonen learning; binary decision trees; clustering; large databases; Chemical analysis; algorithms

Hrčak ID:

127533

URI

https://hrcak.srce.hr/127533

Datum izdavanja:

3.6.2002.

Posjeta: 820 *