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- W2160364128 abstract "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." @default.
- W2160364128 created "2016-06-24" @default.
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- W2160364128 date "1999-12-07" @default.
- W2160364128 modified "2023-09-27" @default.
- W2160364128 title "Clustering Data of Different Information Levels" @default.
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