![](bg8.png)
from each data set at each time point can be done by
other supervised machine learning techniques. In the
rule pruning step, different dissimilarity measurement
can be used to achieve different sets of rules. Or in
the evolutionary trends predicting step, other regres-
sion techniques can be applied to compare with the
existing results. We are also interested in applying
our system to other databases which have the same
properties to test its correctness and effectiveness.
ACKNOWLEDGEMENTS
Viet An’s work has been supported by the Undergrad-
uate Research Experience on Campus (URECA) pro-
gramme from Nanyang Technological University.
REFERENCES
Breiman, L., Friedman, J., Olshen, R., and Stone, C. (1984).
Classification and regression trees. Chapman and
Hall, New York, 368 p.
Cendrowska, J. (1987). Prism: An algorithm for inducing
modular rules. International Journal of Man-Machine
Studies, 27(4):349–370.
Geng, L. and Hamilton, H. J. (2006). Interestingness mea-
sures for data mining: A survey. ACM Computing
Survey, 38(3):9.
Hetland, M. and Saetrom, P. (2002). Temporal rule discov-
ery using genetic programming and specialized hard-
ware. In 4th International Conference on Recent Ad-
vances in Soft Computing, RASC.
Holte, R. (1993). Very simple classification rules per-
form well on most commonly used datasets. Machine
Learning, 11:63–91.
Kadous, M. W. (1999). Learning comprehensible descrip-
tions of multivariate time series. In ICML, pages 454–
463.
Keogh, E. and Kasetty, S. (2002). On the need for time
series data mining benchmarks: a survey and em-
pirical demonstration. In KDD ’02: Proceedings of
the eighth ACM SIGKDD international conference on
Knowledge discovery and data mining, pages 102–
111, New York, NY, USA. ACM.
Kohavi, R. (1995). The power of decision tables. In 8th Eu-
ropean Conference on Machine Learning, pages 174–
189. Springer.
Last, M., Kandel, A., and Bunke, H. (2004). Data Mining in
Time series databases. Series in Machine Perception
and Artificial Intelligence. World Scientific.
Last, M., Klein, Y., and Kandel, A. (2001). Knowledge
discovery in time series databases. IEEE Transactions
on Systems, Man, and Cybernetics, Part B, 31(1):160–
169.
Povinelli, P. (2000). Using genetic algorithms to find tem-
poral patterns indicative of time series events. In
GECCO 2000 Workshop: Data Mining with Evolu-
tionary Algorithsm, pages 80–84.
Quinlan, J. R. (1986). Induction of decision trees. Machine
Learning, 1(1):81–106.
Quinlan, J. R. (1993). C4.5: Programs for machine learn-
ing. Morgan Kaufmann Publishers Inc.
Su, J. and Zhang, H. (2006). A fast decision tree learn-
ing algorithm. In Proceedings of The Twenty-First
National Conference on Artificial Intelligence and the
Eighteenth Innovative Applications of Artificial Intel-
ligence Conference (AAAI/IAAI), July 16-20, 2006,
Boston, Massachusetts, USA.
Tan, P.-N., Kumar, V., and Srivastava, J. (2002). Selecting
the right interestingness measure for association pat-
terns. In KDD ’02: Proceedings of the eighth ACM
SIGKDD international conference on Knowledge dis-
covery and data mining, pages 32–41, New York, NY,
USA. ACM.
Tukey, J. W. (1977). Exploratory Data Analysis. Addison-
Wesley, Reading, MA.
Wah, B. W. and Qian, M. (2002). Constrained formula-
tions and algorithms for stock-price predictions using
recurrent FIR neural networks. In Proceedings of The
Twenty-First National Conference on Artificial Intel-
ligence and the Eighteenth Innovative Applications of
Artificial Intelligence Conference (AAAI/IAAI), pages
211–216, Edmonton, Alberta, Canada.
Wei, L., Kumar, N., Lolla, V. N., Keogh, E. J., Lonardi, S.,
Ratanamahatana, C. A., and Herle, H. V. (2005). A
practical tool for visualizing and data mining medical
time series. In CBMS, pages 341–346.
Weiss, G. and Hirsh, H. (1998). Learning to predict rare
events in event sequences. In 4th Conference on
Knowledge Discovery and Data Mining KDD, pages
359–363, Menlo Park, CA, USA. AAAI Press.
Witten, I. H. and Frank, E. (2005). Data Mining: Practi-
cal Machine Learning Tools and Techniques. Morgan
Kaufmann Series in Data Management Systems. Mor-
gan Kaufmann, second edition.
ICEIS 2008 - International Conference on Enterprise Information Systems
26