Multi-task Data Mining Based on Dynamic Representation Bias

2009/4/1 - 2013/3/31

Research organization
Einoshin Suzuki	Principal investigator, Professor	Department of Informatics, ISEE, Kyushu University

Research abstract:
To effectively cope with multiple pattern discovery tasks related each other, we have developed novel data mining methods each of which automatically modifies representations of data and patterns, implemented them as computer systems, and demonstrated their effectiveness with synthetic and real data. Remarkable achievements are multi-task classification method which employs an extended MDL principle to allow a common dictionary, a multi-task clustering method which employs an extension of information distance based on Kolmogorov complexity, and a dimension reduction method for multi-task data mining.

Selected outcomes:
1. Einoshin Suzuki: "Compression-based Measures for Mining Interesting Rules", Next-Generatation Applied Intelligence (IEA/AIE), LNAI 5579, pp. 741-746, Springer-Verlag, Tainan, Taiwan, 2009 (invited talk at a special session).
2. Daisuke Ikeda, Einoshin Suzuki: Mining Peculiar Compositions of Frequent Substrings from Sparse Text Data using Background Texts, Machine Learning and Knowledge Discovery in Databases (ECML/PKDD), Vol. 1, LNAI 5781, Springer-Verlag, pp. 596-611, September 2009, Bled, Slovenia.
3. JianBin Wang, Bin-Hui Chou, Einoshin Suzuki: Finding the k-Most Abnormal Subgraphs from a Single Graph, Discovery Science, Lecture Notes in Artificial Intelligence 5808 (DS), Springer-Verlag, pp. 441-448, October 2009, Porto, Portugal.
4. Bin Tong and Einoshin Suzuki: "Subclass-oriented Dimension Reduction with Constraint Transformation and Manifold Regularization", Advances in Knowledge Discovery and Data Mining (PAKDD), Part II, LNAI 6119, Springer-Verlag, pp. 1-13, June 2010, Hyderabad, India.
5. Bin Tong, Shao Hao, Bin-Hui Chou, and Einoshin Suzuki: "Semi-Supervised Projection Clustering with Transferred Centroid Regularization", Machine Learning and Knowledge Discovery in Databases (ECML/PKDD), Part III, LNCS 6323, Springer-Verlag, pp. 306-321, September 2010, Barcelona.
6. Bin Tong, ZhiGuang Qin, and Einoshin Suzuki: "Topology Preserving SOM with Transductive Confidence Machine", Discovery Science, Lecture Notes in Artificial Intelligence (DS), LNAI 6332, Springer-Verlag, pp. 27-41, October 2010, Canberra.
7. Einoshin Suzuki: "Discovering a Partial Decision List for Understanding the Controller of a Reactive Robot", Mining patterns and subgroups (MPS). Lorentz Center Workshop, University of Leiden, November 2010, Leiden, Netherlands.
8. Bin-Hui Chou and Einoshin Suzuki: "Role Discovery for Graph Clustering", Web Technologies and Applications (APWeb 2011), pp. 17-28, LNCS 6612, Springer-Verlag, Beijing, April 2011.
9. Bin Tong, Junbin Gao, Nguyen Huy Thach, and Einoshin Suzuki: "Gaussian Process for Dimensionality Reduction in Transfer Learning", Proc. Eleventh SIAM International Conference on Data Mining (SDM 2011), pp. 783-270, Phoenix/Mesa, Arizona, April 2011.
10. Shao Hao and Einoshin Suzuki: "Feature-based Inductive Transfer Learning through Minimum Encoding", Proc. Eleventh SIAM International Conference on Data Mining (SDM 2011), pp. 259-270, Phoenix/Mesa, Arizona, April 2011.
11. Nguyen Huy Thach, Shao Hao, Bin Tong, and Einoshin Suzuki: "A Compression-based Dissimilarity Measure for Multi-task Clustering", Foundations of Intelligent Systems, LNAI 6804 (ISMIS 2011), pp. 123-132, Springer, Warsaw, June 2011.
12. Shao Hao, Bin Tong, and Einoshin Suzuki: "Compact Coding for Hyperplane Classifiers in Heterogeneous Environment", Machine Learning and Knowledge Discovery in Databases (ECML/PKDD), Part III, LNCS 6913, Springer-Verlag, pp. 207-222, September 2011, Athens.
13. Hiroshi Hirai, Bin-Hui Chou, and Einoshin Suzuki: A Parameter-Free Method for Discovering Generalized Clusters in a Network, Discovery Science (DS 2011), LNAI 6926, Springer-Verlag, pp. 135-149, October 2011, Espoo - Helsinki.
14. Shin Ando and Einoshin Suzuki: Role-Behavior Analysis from Trajectory Data by Cross-Domain Learning, Proc. Eleventh IEEE International Conference on Data Mining (ICDM 2011), pp. 21-30, December 2011, Vancouver.
15. Bin Tong, Weifeng Jia, Yanli Ji, and Einoshin Suzuki: "Linear Semi-Supervised Dimensionality Reduction with Pairwise Constraint for Multiple Subclasses", IEICE Transactions on Information and Systems, Vol. E95-D, No. 3, pp. 812-820, March 2012.
16. Bin Tong, Hao Shao, Bin-Hui Chou, and Einoshin Suzuki: "Linear Semi-Supervised Projection Clustering by Transferred Centroid Regularization", Journal of Intelligent Information Systems, Vol. 39, No. 2, pp. 461-490, Springer, October, 2012.
17. Bin-Hui Chou and Einoshin Suzuki: "RoClust: Role Discovery for Graph Clustering", Web Intelligence and Agent Systems, An International Journal, Vol. ?, No. ?, pp. ?-?, IOS Press (accepted for publication).
18. Hao Shao, Bin Tong, and Einoshin Suzuki: "Extended MDL Principle for Feature-Based Inductive Transfer Learning", Knowledge and Information Systems, An International Journal, Vol. ?, No. ?, pp. ?-?, Springer (accepted for publication).
19. Thach Nguyen Huy, Hao Shao, Bin Tong, and Einoshin Suzuki: "A Feature-Free and Parameter-Light Multi-Task Clustering Framework", Knowledge and Information Systems, An International Journal, Vol. ?, No. ?, pp. ?-?, Springer (accepted for publication).
20. Bin Tong, Junbin Gao, Thach Nguyen Huy, Hao Shao, and Einoshin Suzuki: "Transfer Dimensionality Reduction by Gaussian Process in Parallel", Knowledge and Information Systems, An International Journal, Vol. ?, No. ?, pp. ?-?, Springer (accepted for publication).
21. Thach Nguyen Huy, Bin Tong, Hao Shao, and Einoshin Suzuki: "Transfer Learning by Centroid Pivoted Mapping in Noisy Environment", Journal of Intelligent Information Systems, Vol. ?, No. ?, pp. ?-?, Springer (accepted for publication).
22. Bin-Hui Chou and Einoshin Suzuki: "Detecting Academic Plagiarism with Graphs", Extraction et Gestion des Connaissances (EGC'2013), pp. 293-304, Toulouse, France, January 2013.
23. Shin Ando and Einoshin Suzuki: "Time-sensitive Classification of Behavioral Data", Proc. Thireenth SIAM International Conference on Data Mining (SDM 2013), pp. ?-?, Austin, Texas, April 2013 (accepted for publication).