Title :
Clustering the Self-Organizing Map Based on the Neurons´ Associated Pattern Sets
Author :
da Silva, L.E.B. ; Costa, Jose Alfredo Ferreira
Author_Institution :
Programa de Pos-Grad. em Eng. Eletr. e de Comput., Univ. Fed. do Rio Grande do Norte, Natal, Brazil
Abstract :
This paper presents an automatic clustering system, built as a committee machine, which is used to cohesively partition the self-organizing map. In the proposed method, each expert from the committee machine analyzes the connections of the neuron grid based on a particular similarity matrix, and thus decides which ones should be pruned by gradually removing them and observing the intervals of stability. Those intervals are regarded as the ones in which the number of clusters found through connected components remain constant. The output of each expert is a connectivity matrix that effectively expresses which connections should remain as a binary true or false value. The final stage of the committee machine consists of combining the outputs of the experts, and through majority voting establish which connections should remain in the grid, and hence performing the segmentation of the map. The system was evaluated through its application to synthetic and real world data sets.
Keywords :
matrix algebra; pattern clustering; self-organising feature maps; automatic clustering system; binary true; committee machine; connectivity matrix; false value; majority voting; map segmentation; neuron associated pattern sets; neuron grid; real world data sets; self-organizing map; similarity matrix; synthetic data sets; Clustering algorithms; Computational intelligence; Euclidean distance; Neurons; Power system stability; Training; Vectors; clustering; committee machine; self-organizing map; similarity matrix;
Conference_Titel :
Computational Intelligence and 11th Brazilian Congress on Computational Intelligence (BRICS-CCI & CBIC), 2013 BRICS Congress on
Conference_Location :
Ipojuca
DOI :
10.1109/BRICS-CCI-CBIC.2013.13