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Cluster Correspondence Views for Enhanced Analysis of SOM Displays

Bernard, Jürgen and Landesberger, Tatiana von and Bremm, Sebastian and Schreck, Tobias :
Cluster Correspondence Views for Enhanced Analysis of SOM Displays.
In: IEEE Conference on Visual Analytics Science and Technology 2010. Proceedings. IEEE Press, New York
[Conference or Workshop Item] , (2010)

Abstract

The Self-Organizing Map (SOM) algorithm is a popular and widely used cluster algorithm. Its constraint to organize clusters on a grid structure makes it very amenable to visualization. On the other hand, the grid constraint may lead to reduced cluster accuracy and reliability, compared to other clustering methods not implementing this restriction. We propose a visual cluster analysis system that allows to validate the output of the SOM algorithm by comparison with alternative clustering methods. Specifically, visual mappings overlaying alternative clustering results onto the SOM are proposed. We apply our system on an example data set, and outline main analytical use cases.

Item Type: Conference or Workshop Item
Erschienen: 2010
Creators: Bernard, Jürgen and Landesberger, Tatiana von and Bremm, Sebastian and Schreck, Tobias
Title: Cluster Correspondence Views for Enhanced Analysis of SOM Displays
Language: English
Abstract:

The Self-Organizing Map (SOM) algorithm is a popular and widely used cluster algorithm. Its constraint to organize clusters on a grid structure makes it very amenable to visualization. On the other hand, the grid constraint may lead to reduced cluster accuracy and reliability, compared to other clustering methods not implementing this restriction. We propose a visual cluster analysis system that allows to validate the output of the SOM algorithm by comparison with alternative clustering methods. Specifically, visual mappings overlaying alternative clustering results onto the SOM are proposed. We apply our system on an example data set, and outline main analytical use cases.

Publisher: IEEE Press, New York
Uncontrolled Keywords: Forschungsgruppe Visual Search and Analysis (VISA), Visual analytics, Cluster analysis, Self-organizing maps (SOM), Quality measurements
Divisions: Department of Computer Science
Department of Computer Science > Interactive Graphics Systems
Event Title: IEEE Conference on Visual Analytics Science and Technology 2010. Proceedings
Date Deposited: 12 Nov 2018 11:16
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