Data Mining: References Prof. Dr. Karsten Borgwardt, Department - - PowerPoint PPT Presentation

Data Mining: References

• Prof. Dr. Karsten Borgwardt, Department Biosystems, ETH Z¨

urich Basel, Fall Semester 2016

D-BSSE

References I

Achlioptas, P., Sch¨

• lkopf, B., and Borgwardt, K. (2011).

Two-locus association mapping in subquadratic time. In ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), pages 726–734. Azencott, C., Grimm, D., Sugiyama, M., Kawahara, Y., and Borgwardt, K. M. (2013). Efficient network-guided multi-locus association mapping with graph cuts. Bioinformatics, 29(13):171–179. Becker, C., Hagmann, J., M¨ uller, J., Koenig, D., Stegle, O., Borgwardt, K., and Weigel, D. (2011). Spontaneous epigenetic variation in the Arabidopsis thaliana methylome. Nature, 480(7376):245–249.

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References II

Bishop, C. M. (2006). Pattern Recognition and Machine Learning (Information Science and Statistics). Springer-Verlag New York, Inc., Secaucus, NJ, USA. Borgwardt, K. M. (2013). Machine Learning in Computational Biology. Machine Learning Summer School 2013, T¨ ubingen, Germany. Borgwardt, K. M. and Kriegel, H. (2005). Shortest-path kernels on graphs. In Proceedings of the 5th IEEE International Conference on Data Mining (ICDM 2005), 27-30 November 2005, Houston, Texas, USA, pages 74–81.

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References III

Breiman, L. (2001). Random forests. Machine Learning, 45(1):5–32. Breiman, L., Friedman, J. H., Olshen, R. A., and Stone, C. J. (1984). Classification and Regression Trees. Wadsworth. Cao, J., Schneeberger, K., Ossowski, S., G¨ unther, T., Bender, S., Fitz, J., Koenig, D., Lanz, C., Stegle, O., Lippert, C., Wang, X., Ott, F., M¨ uller, J., Alonso-Blanco, C., Borgwardt, K., Schmid, K. J., and Weigel, D. (2011). Whole-genome sequencing of multiple Arabidopsis thaliana populations. Nature Genetics, 43(10):956–963.

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References IV

Cortes, C. and Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3):273–297. Cox, D. R. (1958). The regression analysis of binary sequences (with discussion). J Roy Stat Soc B, 20:215–242. Dempster, A. P., Laird, N. M., and Rubin, D. B. (1977). Maximum likelihood from incomplete data via the em algorithm. JOURNAL OF THE ROYAL STATISTICAL SOCIETY, SERIES B, 39(1):1–38. Donath, W. E. and Hoffman, A. J. (1973). Lower bounds for the partitioning of graphs. IBM J. Res. Dev., 17(5):420–425.

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References V

Ester, M., Kriegel, H., Sander, J., and Xu, X. (1997). Density-connected sets and their application for trend detection in spatial databases. In Proceedings of the Third International Conference on Knowledge Discovery and Data Mining (KDD-97), Newport Beach, California, USA, August 14-17, 1997, pages 10–15. Florek, K., . J. P. J. S. H. Z. S. (1951). Sur la liaison et la division des points d’un ensemble fini. Colloquium Mathematicae, 2(3-4):282–285. Floyd, R. (1962). Algorithm 97, shortest path.

• Comm. ACM, 5:345.

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References VI

G¨ artner, T. (2003). A survey of kernels for structured data. SIGKDD Explorations, 5(1):49–58. Gretton, A., Bousquet, O., Smola, A., and Sch¨

• lkopf, B. (2005).

Measuring statistical dependence with hilbert-schmidt norms. In Proceedings of the 16th International Conference on Algorithmic Learning Theory, ALT’05, pages 63–77, Berlin, Heidelberg. Springer-Verlag. Guyon, I. and Elisseeff, A. (2003). An introduction to variable and feature selection.

• J. Mach. Learn. Res., 3:1157–1182.

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References VII

Hagmann, J., Becker, C., M¨ uller, J., Stegle, O., Meyer, R. C., Wang, G., Schneeberger, K., Fitz, J., Altmann, T., Bergelson, J., Borgwardt, K., and Weigel, D. (2015). Century-scale methylome stability in a recently diverged Arabidopsis thaliana lineage. PLoS Genetics, 11(1):e1004920. Han, J. and Kamber, M. (2006). Data Mining: Concepts and Techniques. The Morgan Kaufmann series in data management systems. Elsevier San Francisco (Calif.), Amsterdam, Boston, Heidelberg. Haussler, D. (1999). Convolution kernels on discrete structures.

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References VIII

Kam-Thong, T., Azencott, C.-A., Cayton, L., P¨ utz, B., Altmann, A., Karbalai, N., S¨ amann, P. G., Sch¨

• lkopf, B., M¨

uller-Myhsok, B., and Borgwardt, K. M. (2012). GLIDE: GPU-based linear regression for detection of epistasis. Human Heredity, 73(4):220–236. Kam-Thong, T., Czamara, D., Tsuda, K., Borgwardt, K., Lewis, C. M., Erhardt-Lehmann, A., Hemmer, B., Rieckmann, P., Daake, M., Weber, F., Wolf, C., Ziegler, A., Putz, B., Holsboer, F., Scholkopf, B., and Muller-Myhsok, B. (2010). EPIBLASTER-fast exhaustive two-locus epistasis detection strategy using graphical processing units. Eur J Hum Genet.

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References IX

Kam-Thong, T., P¨ utz, B., Karbalai, N., M¨ uller-Myhsok, B., and Borgwardt, K. (2011). Epistasis detection on quantitative phenotypes by exhaustive enumeration using GPUs. Bioinformatics (ISMB), 27(13):i214–i221. Karaletsos, T., Stegle, O., Dreyer, C., Winn, J., and Borgwardt, K. M. (2012). ShapePheno: unsupervised extraction of shape phenotypes from biological image collections. Bioinformatics, 28(7):1001–1008. Leslie, C. S., Eskin, E., and Noble, W. S. (2002). The spectrum kernel: A string kernel for SVM protein classification. In Proceedings of the 7th Pacific Symposium on Biocomputing, PSB 2002, Lihue, Hawaii, USA, January 3-7, 2002, pages 566–575.

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References X

Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2):129–136. Murphy, K. P. (2012). Machine learning: a probabilistic perspective. Adaptive computation and machine learning series. MIT Press, Cambridge (Mass.). Nemhauser, G., Wolsey, L., and Fisher, M. (1978). An analysis of approximations for maximizing submodular set functionsi. Mathematical Programming, 14(1):265–294.

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References XI

Ng, A. Y., Jordan, M. I., and Weiss, Y. (2001). On spectral clustering: Analysis and an algorithm. In Advances in Neural Information Processing Systems 14 [Neural Information Processing Systems: Natural and Synthetic, NIPS 2001, December 3-8, 2001, Vancouver, British Columbia, Canada], pages 849–856. Quinlan, J. R. (1986). Induction of decision trees.

• Mach. Learn., 1(1):81–106.

Quinlan, J. R. (1993). C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA.

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References XII

Rousseeuw, P. (1987). Silhouettes: A graphical aid to the interpretation and validation of cluster analysis.

• J. Comput. Appl. Math., 20(1):53–65.

Sch¨

• lkopf, B., Smola, A. J., Williamson, R. C., and Bartlett, P. L. (2000).

New support vector algorithms. Neural Comput., 12(5):1207–1245. Shervashidze, N. and Borgwardt, K. M. (2009). Fast subtree kernels on graphs. In Bengio, Y., Schuurmans, D., Lafferty, J., Williams, C. K. I., and Culotta, A., editors, Advances in Neural Information Processing Systems 22, Proceedings of the Twenty-Third Annual Conference on Neural Information Processing Systems, pages 1660–1668.

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References XIII

Shervashidze, N., Schweitzer, P., van Leeuwen, E., Mehlhorn, K., and Borgwardt,

• K. M. (2011).

Weisfeiler-Lehman graph kernels. Journal of Machine Learning Research, 12:2539–2561. Shi, J. and Malik, J. (2000). Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell., 22(8):888–905. Sugiyama, M. and Borgwardt, K. M. (2013). Rapid distance-based outlier detection via sampling. In Advances in Neural Information Processing Systems 26: 27th Annual Conference

• n Neural Information Processing Systems 2013., pages 467–475.

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References XIV

Sugiyama, M., Lopez, F. L., Kasenburg, N., and Borgwardt, K. M. (2015). Significant subgraph mining with multiple testing correction. In Proceedings of the 2015 SIAM International Conference on Data Mining. in press. Vapnik, V. N. and Chervonenkis, A. Y. (1974). Theory of pattern recognition: Statistical problems of learning [Russian]. Moscow: Nauka. Veropoulos, K., Campbell, C., and Cristianini, N. (1999). Controlling the sensitivity of support vector machines. In Proceedings of the International Joint Conference on AI, pages 55–60.

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References XV

Vishwanathan, S. V. N. and Smola, A. J. (2002). Fast kernels for string and tree matching. In Advances in Neural Information Processing Systems 15 [Neural Information Processing Systems, NIPS 2002, December 9-14, 2002, Vancouver, British Columbia, Canada], pages 569–576. Weston, J., Elisseeff, A., Sch¨

• lkopf, B., and Tipping, M. (2003).

Use of the zero norm with linear models and kernel methods.

• J. Mach. Learn. Res., 3:1439–1461.

Wiener, H. (1947). Structural determination of paraffin boiling points. Journal of the American Chemical Society, 69(1):17–20.

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References XVI

Zaki, M. J. and Jr, W. M. (2014). Data Mining and Analysis: Fundamental Concepts and Algorithms. Cambridge University Press, New York, NY, USA.

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