Research Areas Christoph F. Eick

1
Research Areas Christoph F. Eick

• Supervised Clustering
• Supervised clustering algorithms
• Using supervised clustering to enhance
  classifiers
• Using supervised clustering for region discovery
  in spatial data mining new!
• Adaptive clustering
• Learning subclasses
• Tools for Similarity Assessment and Distance
  Function Learning
• Data Set Compression /Creating Meta Knowledge for
  (Local) Learning Techniques
• Inductive Learning/Data Mining
• Decision trees, nearest neighbor classifiers
• Making sense of data
• Using clustering as a preprocessing step to
  enhance classifiers
• Data Mining and Information Retrieval for
  Structured Data
• Other Evolutionary Computing, File Prediction,
  Ontologies, Heuristic Search, Reinforcement
  Learning, Traditional Clustering, Data Models.

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Research Christoph F. Eick 2005/2006
Clustering for Classification
Editing / Data Set Compression
Supervised Clustering
Distance Function Learning
Spatial Data Mining
Adaptive Clustering
Mining Data Streams Online Data Mining Mining
Sensor Data
Measures of Interestingness
Mining Semi-Structured Data Web Annotation