|
|
-
Aggarwal, C. C. & Yu, P. S. (2001)
- , Outlier detection for high diemnsional data, in Proceedings of the
27th ACM SIGMOD International Conference on Management of Data (SIGMOD01),
pp. 37-46.
-
Agrawal, R. & Srikant, R. (1994),
-
Fast algorithms for mining association rules in large databases, in
J. B. Bocca, M. Jarke & C. Zaniolo, eds, Proceedings of the
20th International Conference on Very Large Databases (VLDB94), Morgan
Kaufmann, pp. 487-499.
http://citeseer.ist.psu.edu/agrawal94fast.html.
-
Barnett, V. & Lewis, T. (1994),
-
Outliers in Statistical Data, John Wiley.
-
Bauer, E. & Kohavi, R. (1999),
- `An
empirical comparison of voting classification algorithms: Bagging, boosting,
and variants', Machine Learning 36(1-2), 105-139.
http://citeseer.ist.psu.edu/bauer99empirical.html.
-
Beyer, K. S., Goldstein, J., Ramakrishnan, R. & Shaft, U.
(1999),
- When is ``nearest neighbor''
meaningful?, in Proceedings of the 7th International Conference on
Database Theory (ICDT99), Jerusalem, Israel, pp. 217-235.
http://citeseer.ist.psu.edu/beyer99when.html.
-
Bhandari, I., Colet, E., Parker, J., Pines, Z., Pratap, R. & Ramanujam, K. (1997),
- `Advance scout: data
mining and knowledge discovery in nba data', Data Mining and Knowledge
Discovery 1(1), 121-125.
-
Blake, C. & Merz, C. (1998),
-
`UCI repository of machine learning databases'.
http://www.ics.uci.edu/~mlearn/MLRepository.html.
-
Breiman, L. (1996),
- `Bagging predictors',
Machine Learning 24(2), 123-140.
http://citeseer.ist.psu.edu/breiman96bagging.html.
-
Breiman, L. (2001),
- `Random forests', Machine Learning 45(1), 5-32.
-
Breunig, M. M., Kriegel, H., Ng, R. & Sander, J. (
1999),
- OPTICS-OF: Identifying local outliers, in Proceedings of the XXXXth Conference on Priciples of Data Mining and
Knowledge Discovery (PKDD99), Springer-Verlag, pp. 262-270.
-
Breunig, M. M., Kriegel, H., Ng, R. & Sander, J. (
2000),
- LOF: Identifying denisty based local outliers, in
Proceedings of the 26th ACM SIGMOD International Conference on
Management of Data (SIGMOD00) Proceedings of the 26th ACM SIGMOD International Conference
on Management of Data (SIGMOD00) (2000).
-
Caruana, R. & Niculescu-Mizil, A. (
2006),
- An empirical comparison of supervised learning
algorithms, in Proceedings of the 23rd International Conference on
Machine Learning, Pittsburgh, PA.
-
Cendrowska, J. (1987),
- `An algorithm for
inducing modular rules', International Journal of Man-Machine Studies
27(4), 349-370.
-
Cleveland, W. S. (1993),
- Visualizing
Data, Hobart Press, Summit, New Jersey.
-
Cook, D. & Swayne, D. F. (2007),
-
Interactive and Dynamic Graphics for Data Analysis, Springer.
-
Culp, M., Johnson, K. & Michailidis, G. (
2006),
- `ada: An r package for stochastic boosting', Journal of Statistical Software 17(2).
http://www.jstatsoft.org/v17/i02/v17i02.pdf.
-
Cypher, A., ed. (1993),
- Watch What I Do:
Programming by Demonstration, The MIT Press, Cambridge, Massachusetts.
http://www.acypher.com/wwid/WWIDToC.html.
-
Dalgaard, P. (2002),
- Introductory
Statistics with R, Statistics and Computing, Springer, New York.
-
Freund, Y. & Mason, L. (1999),
- The
alternating decision tree algorithm, in Proceedings of the 16th
International Conference on Machine Learning, pp. 124-133.
-
Freund, Y. & Schapire, R. E. (1995)
- , A decision-theoretic generalization of on-line learning and an application
to boosting, in Proceedings of the 2nd European Conference on
Computational Learning Theory (Eurocolt95), Barcelona, Spain, pp. 23-37.
http://citeseer.ist.psu.edu/freund95decisiontheoretic.html.
-
Friedman, J. H. (2001),
- `Greedy function
approximation: A gradient boosting machine', Annals of Statistics 29(5), 1189-1232.
http://citeseer.ist.psu.edu/46840.html.
-
Friedman, J. H. (2002),
- `Stochastic gradient
boosting', Computational Statistics and Data Analysis 38(4), 367-378.
http://citeseer.ist.psu.edu/friedman99stochastic.html.
-
Hahsler, M., Grün, B. & Hornik, K. (
2005),
- A Computational Environment for Mining
Association Rules and Frequent Item Sets, R Package, Version 0.2-1.
-
Harrell Jr, F. E. (2007),
- The Design
Package.
R package version 2.1-1.
http://biostat.mc.vanderbilt.edu/s/Design.
-
Hastie, T., Tibshirani, R. & Friedman, J. (
2001),
- The elements of statistical learning: Data
mining, inference, and prediction, Springer Series in Statistics,
Springer-Verlag, New York.
-
Hawkins, D. (1980),
- Identification of
Outliers, Chapman and Hall, London.
-
Ho, T. K. (1998),
- `The random subspace method
for constructing decision forests', IEEE Transactions on Pattern
Analysis and Machine Intelligence 20(8), 832-844.
-
Jin, W., Tung, A. K. H. & Han, J. (
2001),
- Mining top-n local outliers in large databases, in
Proceedings of the 7th International Conference on Knowledge Discovery
and Data Mining (KDD01).
-
King, R. D., Feng, C. & Sutherland, A. (
1995),
- `Statlog: Comparison of classification algorithms on
large real-world problems', Applied Artificial Intellgience 9(3), 289-333.
-
Knorr, E. & Ng, R. (1998),
-
Algorithms for mining distance based outliers in large databases, in Proceedings of the 24th International Conference on Very Large Databases
(VLDB98), pp. 392-403.
-
Knorr, E. & Ng, R. (1999),
- Finding
intensional knowledge of distance-based outliers, in Proceedings of the
25th International Conference on Very Large Databases (VLDB99)
Proceedings of the 25th International Conference on Very
Large Databases (VLDB99) (1999), pp. 211-222.
-
Kohavi, R. (1996),
- Scaling up the accuracy of
naive-Bayes classifiers: A decision tree hybrid, in Proceedings of the
2nd International Conference on Knowledge Discovery and Data Mining (KDD96),
Portland, OR, pp. 202-207.
http://citeseer.ist.psu.edu/kohavi96scaling.html.
-
Lin, W., Orgun, M. A. & Williams, G. J. (
2000),
- Temporal data mining using multilevel-local
polynominal models, in Proceedings of the 2nd International Conference
on Intelligent Data Engineering and Automated Learning (IDEAL 2000), Hong
Kong, Vol. 1983 of Lecture Notes in Computer Science, Springer-Verlag,
pp. 180-186.
-
Lin, W., Orgun, M. A. & Williams, G. J. (
2001),
- Temporal data mining using hidden markov-local
polynomial models, in D. W.-L. Cheung, G. J. Williams & Q. Li, eds, Proceedings of the 5th Pacific-Asia Conference on Knowledge
Discovery and Data Mining (PAKDD01), Hong Kong, Vol. 2035 of Lecture
Notes in Computer Science, Springer-Verlag, pp. 324-335.
-
Mingers, J. (1989),
- `An empirical comparison
of selection measures for decision-tree induction', Machne Learning
3(4), 319-342.
-
Muenchen, R. A. (2008),
- R for SAS and
SPSS Users, Statistics and Computing, Springer.
-
Proceedings of the 25th International Conference on Very Large Databases
(VLDB99) (1999).
-
-
Proceedings of the 26th ACM SIGMOD International Conference on Management
of Data (SIGMOD00) (2000),
- ACM Press.
-
Provost, F. J., Jensen, D. & Oates, T. (
1999),
- Efficient progressive sampling, in Proceedings
of the 5th International Conference on Knowledge Discovery and Data Mining
(KDD99), San Diego, CA, ACM Press, pp. 23-32.
http://citeseer.ist.psu.edu/provost99efficient.html.
-
Quinlan, J. R. (1993),
- C4.5: Programs
for machine learning, Morgan Kaufmann.
-
R D (2005),
- R Data Import/Export,
version 2.1.1 edn.
-
Ramaswamy, S., Rastogi, R. & Kyuseok, S. (
2000),
- Efficient algorithms for mining outliers from large
data sets, in Proceedings of the 26th ACM SIGMOD International
Conference on Management of Data (SIGMOD00) Proceedings of the 26th ACM SIGMOD International Conference
on Management of Data (SIGMOD00) (2000),
pp. 427-438.
-
Schafer, J. L. (1997),
- Analysis of
Incomplete Multivariate Data, Chapman and Hall, London.
-
Schapire, R. E., Freund, Y., Bartlett, P. & Lee, W. S.
(1997),
- Boosting the margin: a new
explanation for the effectiveness of voting methods, in Proceedings of
the 14th International Conference on Machine Learning (ICML97), Morgan
Kaufmann, pp. 322-330.
http://citeseer.ist.psu.edu/schapire97boosting.html.
-
Soares, C., Brazdil, P. B. & Kuba, P. (
2004),
- `Meta-learning method to select the kernel width in
support vector regression', Machine Learning 54(3), 195-209.
-
Tufte, E. R. (1985),
- The Visual Display
of Quantitative Information, Graphics Press.
A timeless classic in how complex information should be presented
graphically. The Strunk & White of visual design. Should occupy a place of
honor-within arm's reach-of everyone attempting to understand or depict
numerical data graphically. The design of the book is an exemplar of the
principles it espouses: elegant typography and layout, and seamless
integration of lucid text and perfectly chosen graphical examples. Very
Highly Recommended.
-
Tukey, J. W. (1977),
- Exploratory data
analysis, Addison-Wesley.
-
Venables, W. N. & Ripley, B. D. (
2002),
- Modern Applied Statistics with S, Staistics and
Computing, 4th edn, Springer, New York.
-
Viveros, M. S., Nearhos, J. P. & Rothman, M. J. (
1999),
- Applying data mining techniques to a health insurance
information system., in Proceedings of the 25th International Conference
on Very Large Databases (VLDB99) Proceedings of the 25th International Conference on Very
Large Databases (VLDB99) (1999), pp. 286-294.
http://www.informatik.uni-trier.de/~ley/vldb/ViverosNR96/Article.PS.
-
Williams, G. J. (1987),
- `Some experiments in
decision tree induction.', Australian Computer Journal 19(2), 84-91.
-
Williams, G. J. (1988),
- Combining decision
trees: Initial results from the MIL algorithm, in J. S. Gero
& R. B. Stanton, eds, Artificial Intelligence Developments
and Applications, Elsevier Science Publishers B.V. (North-Holland),
pp. 273-289.
-
Williams, G. J. (1991),
- Inducing and
combining decision structures for expert systems, PhD thesis, Australian
National University.
http://togaware.redirectme.net/papers/gjwthesis.pdf.
-
Yamanishi, K., ichi Takeuchi, J., Williams, G. J. & Milne, P.
(2000),
- On-line unsupervised outlier
detection using finite mixtures with discounting learning algorithms, in Proceedings of the 6th International Conference on Knowledge Discovery and
Data Mining (KDD00), pp. 320-324.
http://citeseer.ist.psu.edu/446936.html.
-
Ye, J. (1998),
- `On measuring and correcting
the effects of data mining and model selection', Journal of the American
Statistical Association 93(441), 120-131.
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