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Clustering Data of Different Information Levels

Please always quote using this URN: urn:nbn:de:0297-zib-4319
  • For using Data Mining, especially cluster analysis, one needs measures to determine the similarity or distance between data objects. In many application fields the data objects can have different information levels. In this case the widely used euclidean distance is an inappropriate measure. The present paper describes a concept how to use data of different information levels in cluster analysis and suggests an appropriate similarity measure. An example from practice is included, that shows the usefulness of the concept and the measure in combination with {\sc Kohonens} Self-Organizing Map algorithm, a well-known and powerful tool for cluster analysis.

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Metadaten
Author:Tobias Galliat
Document Type:ZIB-Report
Tag:Data Mining; Self-Organizing Maps; cluster analysis; data preprocessing; information theory; missing values; similarity measures
MSC-Classification:62-XX STATISTICS / 62Hxx Multivariate analysis [See also 60Exx] / 62H30 Classification and discrimination; cluster analysis [See also 68T10]
68-XX COMPUTER SCIENCE (For papers involving machine computations and programs in a specific mathematical area, see Section -04 in that area) / 68Txx Artificial intelligence / 68T05 Learning and adaptive systems [See also 68Q32, 91E40]
94-XX INFORMATION AND COMMUNICATION, CIRCUITS / 94Axx Communication, information / 94A17 Measures of information, entropy
Date of first Publication:1999/12/07
Series (Serial Number):ZIB-Report (SC-99-42)
ZIB-Reportnumber:SC-99-42
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