Verifying the Proximity Hypothesis for Self-Organizing Maps (2000)

Content Provider	CiteSeerX
Author	Lin, Chienting Chen, Hsinchun Nunamaker, Jay F.
Abstract	The Kohonen Self-Organizing Map (SOM) is an unsupervised learning technique for summarizing high-dimensional data so that similar inputs are, in general, mapped close to each other. When applied to textual data, SOM has been shown to be able to group together related concepts in a data collection. This article presents research in which we sought to validate this property of SOM, called the Proximity Hypothesis, through a user evaluation study. Built upon our previous research in automatic concept generation and classification, we demonstrated that the Kohonen SOM was able to perform concept clustering effectively, based on its concept precision and recall scores judged by human experts. We believe this research has established the Kohonen SOM algorithm as an intuitively appealing and promising neural network based textual classification technique for addressing part of the long-standing “information overload ” problem.
File Format	PDF
Volume Number	16
Journal	Journal of Management Information Systems
Language	English
Publisher Date	2000-01-01
Access Restriction	Open
Subject Keyword	Proximity Hypothesis Self-organizing Map Related Concept Data Collection Kohonen Som Automatic Concept Generation Textual Data Kohonen Self-organizing Map Long-standing Information Overload Problem Human Expert Concept Precision Unsupervised Learning Technique Similar Input Kohonen Som Algorithm Promising Neural Network User Evaluation Study Previous Research High-dimensional Data Textual Classification Technique Recall Score
Content Type	Text
Resource Type	Article