Computational methods for rough classification and discovery
Journal of the American Society for Information Science - Special issue: knowledge discovery and data mining
Rough computational methods for information systems
Artificial Intelligence
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Trend Graphs: Visualizing the Evolution of Concept Relationships in Large Document Collections
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
TextVis: An Integrated Visual Environment for Text Mining
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
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In the large collections of genomic information accumulated in recent years there is potentially significant knowledge for exploitation in medicine and in the pharmaceutical industry. One interesting approach to the distillation of such knowledge is to detect strings in DNA sequences which are very repetitive within a given sequence (eg for a particular patient) or across sequences (eg from different patients who have been classified in some way eg as sharing a particular medical diagnosis). Motifs are strings that occur relatively frequently. In this paper we present basic theory and algorithms for finding such frequent and common strings. We are particularly interested in strings which are maximally frequent and, having discovered very frequent motifs we show how to mine association rules by an existing rough sets based technique. Further work and applications are in process.