Self-Organizing Maps of Large Document Collections

1998 ◽  
pp. 168-178 ◽  
Author(s):  
Timo Honkela ◽  
Krista Lagus ◽  
Samuel Kaski
Keyword(s):  
Self Organizing Maps ◽  
Document Collections ◽  
Self Organizing

scholarly journals Document Clustering using Self-Organizing Maps

MENDEL ◽  
2017 ◽  
Vol 23 (1) ◽  
pp. 111-118
Author(s):  
Muhammad Rafi ◽  
Muhammad Waqar ◽  
Hareem Ajaz ◽  
Umar Ayub ◽  
Muhammad Danish
Keyword(s):  
Document Clustering ◽  
Feature Space ◽  
Good Method ◽  
Self Organization ◽  
Self Organizing Maps ◽  
Document Collections ◽  
Document Collection ◽  
Common Technique ◽  
Low Dimensional ◽  
Self Organizing

Cluster analysis of textual documents is a common technique for better ltering, navigation, under-standing and comprehension of the large document collection. Document clustering is an autonomous methodthat separate out large heterogeneous document collection into smaller more homogeneous sub-collections calledclusters. Self-organizing maps (SOM) is a type of arti cial neural network (ANN) that can be used to performautonomous self-organization of high dimension feature space into low-dimensional projections called maps. Itis considered a good method to perform clustering as both requires unsupervised processing. In this paper, weproposed a SOM using multi-layer, multi-feature to cluster documents. The paper implements a SOM usingfour layers containing lexical terms, phrases and sequences in bottom layers respectively and combining all atthe top layers. The documents are processed to extract these features to feed the SOM. The internal weightsand interconnections between these layers features(neurons) automatically settle through iterations with a smalllearning rate to discover the actual clusters. We have performed extensive set of experiments on standard textmining datasets like: NEWS20, Reuters and WebKB with evaluation measures F-Measure and Purity. Theevaluation gives encouraging results and outperforms some of the existing approaches. We conclude that SOMwith multi-features (lexical terms, phrases and sequences) and multi-layers can be very e ective in producinghigh quality clusters on large document collections.

WEBSOM – Self-organizing maps of document collections

1998 ◽  
Vol 21 (1-3) ◽  
pp. 101-117 ◽  
Author(s):  
Samuel Kaski ◽  
Timo Honkela ◽  
Krista Lagus ◽  
Teuvo Kohonen
Keyword(s):  
Self Organizing Maps ◽  
Document Collections ◽  
Self Organizing

A Psychographic Segmentation of Kuwaiti Travelers Using Self-Organizing Maps

2019 ◽  
Vol 24 (1) ◽  
pp. 87-92 ◽  
Author(s):  
Yvette Reisinger ◽  
Mohamed M. Mostafa ◽  
John P. Hayes
Keyword(s):  
Self Organizing Maps ◽  
Self Organizing

Self Organizing Maps, Pattern Recognition and Financial Crises

2011 ◽  
Author(s):  
Sylvain Barthelemy ◽  
Pascal Devaux ◽  
Francois Faure ◽  
Matthieu Pautonnier
Keyword(s):  
Pattern Recognition ◽  
Financial Crises ◽  
Self Organizing Maps ◽  
Self Organizing
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