Marginal median SOM for document organization and retrieval.
The self-organizing map algorithm has been used successfully in document organization. We now propose using the same algorithm for document retrieval. Moreover, we test the performance of the self-organizing map by replacing the linear Least Mean Squares adaptation rule with the marginal median. We present two implementations of the latter variant of the self-organizing map by either quantifying the real valued feature vectors to integer valued ones or not. Experiments performed using both implementations demonstrate a superior performance against the self-organizing map based method in terms of the number of training iterations needed so that the mean square error (i.e. the average distortion) drops to the e(-1) = 36.788% of its initial value. Furthermore, the performance of a document organization and retrieval system employing the self-organizing map architecture and its variant is assessed using the average recall-precision curves evaluated on two corpora; the first comprises of manually selected web pages over the Internet having touristic content and the second one is the Reuters-21578, Distribution 1.0.
Georgakis
A
A
Artificial Intelligence and Information Analysis Laboratory, Department of Informatics, Aristotle University of Thessaloniki, Box 451, Thessaloniki GR-54124, Greece. apostolos.georgakis@tfe.umu.se
Kotropoulos
C
C
Xafopoulos
A
A
Pitas
I
I
D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D003627:Data Interpretation, Statistical; D006801:Humans; D016247:Information Storage and Retrieval; D008377:Maps as Topic; D008606:Mental Processes / Q000502:physiology*; D008959:Models, Neurological; D016571:Neural Networks, Computer; D012815:Signal Processing, Computer-Assisted