Local Induction of Decision Trees: Towards Interactive Data Mining

Proceedings - 2nd International Conference on Knowledge Discovery and Data Mining, KDD 1996

Volumn , Issue , 1996, Pages 14-19

  Fulton, Truxton ^a   Kasif, Simon ^a   Salzberg, Steven ^a   Waltzt, David ^b  

^a Johns Hopkins University   (United States)

^b Fnec Research Institute   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; CLASSIFICATION (OF INFORMATION); DATA MINING; QUERY PROCESSING;

CLASSIFICATION TECHNIQUE; COMPLEX DATA; DATA BASE; DATA-MINING TOOLS; GENERALISATION; GLOBAL APPROACHES; INTERACTIVE DATA MINING; LOCAL DATA; NEIGHBOURHOOD; QUERY POINTS;

DECISION TREES;

EID: 0342928254     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (18)

1. Christopher Atkeson, Andrew Moore, and Stefan Schaal. Locally weighted learning. Artificial Intelligence Review, 1996 (to appear).
2. C. Atkeson. Using local models to control movement. In Neural Information Processing Systems Conf., 1989.
3. L. Breiman, J. Friedman, R. Olshen, and C. Stone. Classification and Regression Trees. Wadsworth Internal. Group,1984.
4. L. Bottou and V. Vapnik. Local learning algorithms. Neural Computation, 4:888-900, 1992.
5. R. Creecy, B. Masand, S. Smith, and D. L. Waltz. Trading MIPS and memory for knowledge engineering. Communications of the ACM, 35(8):48-64, 1992.
6. S. Cost and S. Salzberg. A weighted nearest neighbor algorithm for learning with symbolic features. Machine Learning, 10(1):57-78, 1993.
7. S. Edelman. Representation, similarity and the chorus of prototypes. Minds and Machines, 1994.
8. T. Fulton, S. Kasif, S. Salzberg, and D. Waltz. Local induction of decision trees: Towards interactive data mining. Technical report, Johns Hopkins University, Baltimore, MD 21218, 1996.
9. J. H. Friedman. Flexible metric nearest neighbor classification. Technical report, Stanford University, Statistics Dept., November 1994.
10. T. Hastie and R. Tibshirani. Discriminant adaptive nearest neighbor classification. Technical report, Stanford University, Statistics Dept., December 1994.
11. A. Moore, C. Atkeson, and S. Schaal. Memory-based learning for control. Artificial Intelligence Review (to appear), 1995.
12. P. Maes and R. Kozierok. Learning interface agents. In Proc. of the Eleventh National Conf. on Artificial Intelligence, pages 459-465, Washington, D.C., 1993. MIT Press.
13. Sreerama K. Murthy, Simon Kasif, and Steven Salzberg. A system for induction of oblique decision trees. Journal of Artificial Intelligence Research, 2:133, August 1994.
14. B. Masand, G. Linoff, and D. L. Waltz. Classifying news stories using memory based reasoning. In Proceedings of the SIGIR, pages 59-65, 1992.
15. F. P. Preparata and M. I. Shamos. Computational Geometry: An Introduction. Springer-Verlag, New York, NY, 1985.
16. J. R. Quinlan. C4-5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Mateo, CA, 1993.
17. V. Vapnik and L. Bottou. Local learning algorithms for pattern recognition and dependency estimation. Neural Computation, 5:893-909, 1993.
18. T.-M. Yi and E. Lander. Protein secondary structure prediction using nearest-neighbor methods. Journal of Molecular Biology, 232:1117-1129, 1993.