Rattle and Other Data Mining Tales

Chapter

Abstract

My own voyage to data mining started long before data mining had a name. It started as a curiosity that a young scientist had in searching for interesting patterns in data. In fact, the journey began in 1983 as an artificial intelligence Ph.D. student at the Australian National University, under Professor Robin Stanton.

Keywords

Data Mining Expert System Personal Server Multivariate Adaptive Regression Spline Data Mining Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    S. Bakin, M. Hegland, G.J. Williams, Mining taxation data with parallel bmars. Parallel Algorithm. Appl. 15, 37–55 (2000)MathSciNetCrossRefGoogle Scholar
  2. 2.
    R.M. Bell, J. Bennett, Y. Koren, C. Volinsky, The million dollar programming prize. IEEE Spectr. 46, 28–33 (2009)CrossRefGoogle Scholar
  3. 3.
    M.R. Berthold, N. Cebron, F. Dill, T.R. Gabriel, T. Kötter, T. Meinl, P. Ohl, C. Sieb, K. Thiel, B. Wiswedel, KNIME: The Konstanz Information Miner. Studies in Classification, Data Analysis, and Knowledge Organization (GfKL 2007) (Springer, Heidelberg, 2007)Google Scholar
  4. 4.
    L. Breiman, Random forests. Mach. Learn. 45(1), 5–32 (2001)MATHCrossRefGoogle Scholar
  5. 5.
    L. Breiman, J. Friedman, R. Olshen, C. Stone, Classification and Regression Trees (Wadsworth and Brooks, Monterey, CA, 1984)MATHGoogle Scholar
  6. 6.
    P. Compton, R. Jansen, Knowledge in context: a strategy for expert system maintenance, in Proceedings of the 2nd Australian Joint Conference on Artificial Intelligence (1988), pp. 292–306Google Scholar
  7. 7.
    J.R. Davis, P.M. Nanninga, G.J. Williams, Geographic expert systems for resource management, in Proceedings of the First Australian Conference on Applications of Expert Systems (Sydney, Australia, 1985)Google Scholar
  8. 8.
    Y. Freund, R.E. Schapire, A decision-theoretic generalization of on-line learning and an application to boosting, in Proceedings of the Second European Conference on Computational Learning Theory (Springer, London, 1995), pp. 23–37Google Scholar
  9. 9.
    A. Guazzelli, W.-C. Lin, T. Jena, PMML in Action, CreateSpace (2010)Google Scholar
  10. 10.
    A. Guazzelli, M. Zeller, W.-C. Lin, G. Williams, Pmml: an open standard for sharing models. R J. 1(1), 60–65 (2009). http://journal.r-project.org/2009-1/RJournalfi2009-1fiGuazzelli+et+al.pdf Google Scholar
  11. 11.
    I. Mierswa, M. Wurst, R. Klinkenberg, M. Scholz, T. Euler, Yale: rapid prototyping for complex data mining tasks, in KDD ’06: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ed. by L. Ungar, M. Craven, D. Gunopulos, T. Eliassi-Rad (ACM, Philadelphia, PA, 2006), pp. 935–940Google Scholar
  12. 12.
    J.R. Quinlan, Induction of decision trees. Mach. Learn. 1(1), 81–106 (1986)Google Scholar
  13. 13.
    R (1993) A Language and Environment for Statistical Computing, Open Source, http://www.R-project.org
  14. 14.
    G.A. Riessen, G.J. Williams, X. Yao, Pepnet: parallel evolutionary programming for constructing artificial neural networks, in Evolutionary Programming VI, ed. by P.J. Angeline, R.G. Reynolds, J.R. McDonnell, R. Eberhart. Lecture Notes in Computer Science, vol. 1213 (Springer, Indianapolis, IN, 1997), pp. 35–46CrossRefGoogle Scholar
  15. 15.
    G.J. Williams, Some experiments in decision tree induction. Aust. Comput. J. 19(2), 84–91 (1987). http://togaware.com/papers/acj87fidtrees.pdf Google Scholar
  16. 16.
    G.J. Williams, Combining decision trees: initial results from the MIL algorithm, in: Artificial Intelligence Developments and Applications: Selected papers from the first Australian Joint Artificial Intelligence Conference, Sydney, Australia, 2–4 November, 1987, ed. by J.S. Gero, R.B. Stanton (Elsevier Science Publishers B.V., North-Holland, 1988), pp. 273–289Google Scholar
  17. 17.
    G.J. Williams, Frameup: a frames formalism for expert systems. Aust. Comput. J. 21(1), 33–40 (1989). http://togaware.com/papers/acj89fiheffe.pdf Google Scholar
  18. 18.
    G.J. Williams, Inducing and combining decision structures for expert systems, Ph.D. thesis, Australian National University, 1991, http://togaware.com/papers/gjwthesis.pdf
  19. 19.
    G.J. Williams, Rattle: a data mining GUI for R. R J. 1(2), 45–55 (2009). http://journal.r-project.org/archive/2009-2/RJournalfi2009-2fiWilliams.pdf Google Scholar
  20. 20.
    G.J. Williams, Data Mining with Rattle and R: The Art of Excavating Data for Knowledge Discovery. Use R! (Springer, New York, 2011)MATHGoogle Scholar
  21. 21.
    G.J. Williams, J.R. Davis, P.M. Nanninga, Gem: a microcomputer based expert system for geographic domains, in Proceedings of the Sixth International Workshop and Conference on Expert Systems and Their Applications (Avignon, France, 1986), Winner of the best student paper awardGoogle Scholar
  22. 22.
    G.J. Williams, Z. Huang, Mining the knowledge mine: the hot spots methodology for mining large real world databases, in Advanced Topics in Artificial Intelligence, ed. by A. Sattar (Springer, London, 1997), pp. 340–348CrossRefGoogle Scholar
  23. 23.
    I.H. Witten, E. Frank, Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn. (Morgan Kaufmann, San Francisco, CA, 2005). http://www.cs.waikato.ac.nz/~ml/weka/book.html MATHGoogle Scholar
  24. 24.
    K. Yamanishi, J-i Takeuchi, G.J. Williams, P. Milne, Online unsupervised outlier detection using finite mixtures with discounting learning algorithms. Data Min. Knowl. Discov. 8, 275–300 (2004)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Togaware Pty Ltd.CanberraAustralia

Personalised recommendations