Mining clusters and corresponding interpretable descriptions - A three-stage approach

Mario Drobics, Ulrich Bodenhofer, Werner Winiwarter

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)


This paper presents a three-stage approach to data mining which puts special emphasis on the visualization and interpretability of the results. In the first stage, the input data are represented by a self-organizing map in order to allow visualization and to reduce the amount of data while removing noise, outliers and missing values. Then this preprocessed information is used to identify and display fuzzy clusters of similarity. Finally, descriptions close to natural language are computed for these clusters in order to provide the analyst with qualitative information. This is accomplished by generating fuzzy rules using an inductive learning method. The proposed approach is applied to three case studies, including image data and real-world data sets. The results illustrate the robustness, intuitiveness and wide applicability of the method.

Original languageEnglish
Pages (from-to)224-234
Number of pages11
JournalExpert Systems
Issue number4
Publication statusPublished - Sept 2002
Externally publishedYes


  • Clustering
  • Data analysis
  • Fuzzy logic
  • Inductive learning
  • Self-organizing map


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