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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
A motto borrowed from the Australian Taxation Office: http://www.ato.gov.au/corporate/content.asp?doc=/content/78950.htm .
- 2.
References
S. Bakin, M. Hegland, G.J. Williams, Mining taxation data with parallel bmars. Parallel Algorithm. Appl. 15, 37–55 (2000)
R.M. Bell, J. Bennett, Y. Koren, C. Volinsky, The million dollar programming prize. IEEE Spectr. 46, 28–33 (2009)
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)
L. Breiman, Random forests. Mach. Learn. 45(1), 5–32 (2001)
L. Breiman, J. Friedman, R. Olshen, C. Stone, Classification and Regression Trees (Wadsworth and Brooks, Monterey, CA, 1984)
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. 292–306
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)
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. 23–37
A. Guazzelli, W.-C. Lin, T. Jena, PMML in Action, CreateSpace (2010)
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
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. 935–940
J.R. Quinlan, Induction of decision trees. Mach. Learn. 1(1), 81–106 (1986)
R (1993) A Language and Environment for Statistical Computing, Open Source, http://www.R-project.org
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. 35–46
G.J. Williams, Some experiments in decision tree induction. Aust. Comput. J. 19(2), 84–91 (1987). http://togaware.com/papers/acj87fidtrees.pdf
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. 273–289
G.J. Williams, Frameup: a frames formalism for expert systems. Aust. Comput. J. 21(1), 33–40 (1989). http://togaware.com/papers/acj89fiheffe.pdf
G.J. Williams, Inducing and combining decision structures for expert systems, Ph.D. thesis, Australian National University, 1991, http://togaware.com/papers/gjwthesis.pdf
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
G.J. Williams, Data Mining with Rattle and R: The Art of Excavating Data for Knowledge Discovery. Use R! (Springer, New York, 2011)
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 award
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. 340–348
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
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Williams, G.J. (2012). Rattle and Other Data Mining Tales. In: Gaber, M. (eds) Journeys to Data Mining. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28047-4_15
Download citation
DOI: https://doi.org/10.1007/978-3-642-28047-4_15
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-28046-7
Online ISBN: 978-3-642-28047-4
eBook Packages: Computer ScienceComputer Science (R0)