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Information distance in multiples
| Content Provider | CiteSeerX |
|---|---|
| Author | Vitányi, Paul M. B. |
| Abstract | Abstract—Information distance is a parameter-free similarity measure based on compression, used in pattern recognition, data mining, phylogeny, clustering and classification. The notion of information distance is extended from pairs to multiples (finite lists). We study maximal overlap, metricity, universality, minimal overlap, additivity and normalized information distance in multiples. We use the theoretical notion of Kolmogorov complexity which for practical purposes is approximated by the length of the compressed version of the file involved, using a real-world compression program. Index Terms—Data mining, information distance, Kolmogorov complexity, multiples, pattern recognition, similarity. |
| File Format | |
| Journal | IEEE Trans. Inform. Theory |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Information Distance Pattern Recognition Kolmogorov Complexity Parameter-free Similarity Measure Compressed Version Real-world Compression Program Finite List Abstract Information Distance Maximal Overlap Index Term Data Mining Normalized Information Distance Data Mining Minimal Overlap Theoretical Notion Practical Purpose |
| Content Type | Text |
| Resource Type | Article |
