In scheduling of airport operations the unreliability of flight arrivals is a serious challenge. Robustness with respect to flight delay is incorporated into recent scheduling techniques. To refine proactive scheduling, we propose classification of flights into delay categories. Our method is based on archived data at major airports in current flight information systems. Classification in this scenario is hindered by the large number of attributes, that might occlude the dominant patterns of flight delays. As not all of these attributes are equally relevant for different patterns, global dimensionality reduction methods are not appropriate.We therefore present a technique which identifies locally relevant attributes for the classification into flight delay categories. We give an algorithm that efficiently identifies relevant attributes. Our experimental evaluation demonstrates that our technique is capable of detection relevant patterns useful for flight delay classification.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
M. Abdel-Aty, C. Lee, Y. Bai, X. Li, and M. Michalak. Detecting periodic patterns of arrival delay. Journal of Air Transport Management, pages 355 – 361, 2007.
R. Agrawal, J. Gehrke, D. Gunopulos, and P. Raghavan. Automatic subspace clustering of high dimensional data for data mining applications. In Proceedings of the ACM International Conference on Management of Data (SIGMOD), pages 94 – 105, 1998.
R. Agrawal and R. Srikant. Fast algorithms for mining association rules. In Proceedings of the International Conference on Very Large Data Bases (VLDB), pages 487 – 499, 1994.
I. Assent, R. Krieger, B. Glavic, and T. Seidl. Clustering multidimensional sequences in spatial and temporal databases. International Journal on Knowledge and Information Systems (KAIS), 2008.
I. Assent, R. Krieger, E. Müller, and T. Seidl. DUSC: Dimensionality unbiased subspace clustering. In Proceedings of the IEEE International Conference on Data Mining (ICDM), pages 409 – 414, 2007.
. I. Assent, R. Krieger, P. Welter, J. Herbers, and T. Seidl. Subclass: Classification of multidimensional noisy data using subspace clusters. In Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Osaka, Japan. Springer, 2008.
T. Bayes. An essay towards solving a problem in the doctrine of chances. Philosophical Transactions of the Royal Society, 53:370 – 418, 1763.
K. Beyer, J. Goldstein, R. Ramakrishnan, and U. Shaft. When is nearest neighbors meaningful. In Proceedings of the 7th International Conference on Database Theory (ICDT), pages 217 – 235, 1999.
A. Bolat. Procedures for providing robust gate assignments for arriving aircrafts. European Journal of Operational Research, 120:63 – 80, 2000.
Bureau of Transportation Statistics. Airline on-time performance data. Available from http://www.transtats.bts.gov.
Y. Cao and J. Wu. Projective art for clustering data sets in high dimensional spaces. Neural Networks, 15(1):105 – 120, 2002.
C. Domeniconi, J. Peng, and D. Gunopulos. Locally adaptive metric nearest-neighbor classification. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 24(9):1281 – 1285, 2002.
J. Dong, A. Krzyak, and C. Suen. Fast SVM Training Algorithm with Decomposition on Very Large Data Sets. IEEE Transactions Pattern Analysis and Machine Intelligence (PAMI), pages 603 – 618, 2005.
U. Dorndorf, F. Jaehn, and E. Pesch. Modelling robust flight-gate scheduling as a clique partitioning problem. Transportation Science, 2008.
R. Duda, P. Hart, and D. Stork. Pattern Classification (2nd Edition). Wiley, 2000.
Eurocontrol Central Office for Delay Analysis. Delays to air transport in europe. Available from http://www.eurocontrol.int/eCoda.
R. Gray. Entropy and Information Theory. Springer, 1990.
S. Hettich and S. Bay. The UCI KDD archive [http://kdd.ics.uci.edu]. Irvine, CA: University of California, Department of Information and Computer Science, 1999.
A. Hinneburg, C. Aggarwal, and D. Keim. What is the nearest neighbor in high dimensional spaces? In Proceedings of the International Conference on Very Large Data Bases (VLDB), pages 506 – 515, September 2000.
I. Joliffe. Principal Component Analysis. Springer, New York, 1986.
K. Kailing, H.-P. Kriegel, and P. Kröger. Density-connected subspace clustering for high-dimensional data. In Proceedings of the IEEE International Conference on Data Mining (ICDM), pages 246 – 257, 2004.
S. Lan, J.-P. Clarke, and C. Barnhart. Planning for robust airline operations: Optimizing aircraft routings and flight departure times to minimize passenger disruptions. Transportation Science, 40(1):15–28, 2006.
W. McCulloch and W. Pitts. A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, 5:115 – 137, 1943.
S. Murthy. Automatic construction of decision trees from data: A multi-disciplinary survey. Data Mining and Knowledge Discovery, 2(4):345 – 389, 1998.
L. Parsons, E. Haque, and H. Liu. Subspace clustering for high dimensional data: a review. SIGKDD Explorations Newsletter, 6(1):90 – 105, 2004.
E. Patrick and F. Fischer. A generalized k-nearest neighbor rule. Information and Control, 16(2):128 – 152, 1970.
J. Platt. Fast training of support vector machines using sequential minimal optimization. In Schoelkopf, Burges, and Smola, editors, Advances in Kernel Methods. MIT Press, 1998.
J. Quinlan. Induction of decision trees. Machine Learning, 1:81 – 106, 1986.
J. Quinlan. C4.5: Programs for Machine Learning. Morgan Kaufmann, 1992.
K. Sequeira and M. Zaki. SCHISM: A new approach for interesting subspace mining. In Proceedings of the IEEE International Conference on Data Mining (ICDM), pages 186 – 193, 2004.
C. Shannon and W. Weaver. The Mathematical Theory of Communication. University of Illinois Press, Urbana, Illinois, 1949.
L. Silva, J. M. de Sa, and L. Alexandre. Neural network classification using Shannon? Entropy. In Proceedings of the European Symposium on Artificial Neural Networks (ESANN), 2005.
M. Zaki, M. Peters, I. Assent, and T. Seidl. Clicks: An effective algorithm for mining subspace clusters in categorical datasets. Data & Knowledge Engineering (DKE), 57, 2007.
H. Zhang, A. Berg, M. Maire, and J. Malik. SVM-KNN: Discriminative nearest neighbor classification for visual category recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2, 2006.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer Science+Business Media, LLC
About this chapter
Cite this chapter
Assent, I., Krieger, R., Welter, P., Herbers, J., Seidl, T. (2009). Data Mining For Robust Flight Scheduling. In: Cao, L., Yu, P.S., Zhang, C., Zhang, H. (eds) Data Mining for Business Applications. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-79420-4_19
Download citation
DOI: https://doi.org/10.1007/978-0-387-79420-4_19
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-79419-8
Online ISBN: 978-0-387-79420-4
eBook Packages: Computer ScienceComputer Science (R0)