Prospectus

25-26,24-25,23-24,22-23,21-22,19-20,18-19,17-18

Level

MSc, 6 EC

Part of

MSc Computer Science

Lecturers

Matthijs van Leeuwen, Lincen Yang

Introduction

How can we gain insight from data? How can we discover and explain structure in data if we don't know what to expect? What is the optimal model for our data? How do we develop principled algorithms for exploratory data mining? To answer these questions, we study and discuss the state of the art in the relatively young research area of information theoretic data mining. We focus on theory, problems, and algorithms, not on implementation and experimentation.

Contents and schedule

The following provides a general overview of the contents and schedule of the course; abbreviations for class types are explained below. Further details, slides, and literature will be made available on Brightspace.

#	Type	Topic	Mandatory	Optional
1	L	Introduction	[6](Section 1)	[1](Ch1-6), [2](1.2-1.6)
2	L	Kolmogorov complexity	[4,6]	[5], [2](1.7-1.8,2-2.1.1,8.3-8.3.3,8.4)
3	L	The Minimum Description Length principle	[3](Ch1-3,5)
4	L	Pattern-based modelling	[7]	[8,19]
5	S	Coding for exploratory data analysis	[7,9,10]	[11-13]
6	S	Finding good models	[7,10,14]	[11,15]
7	L	Subjective interestingness	[16]	[17,18]
8	P	Presentations #1
9	P	Presentations #2
10	P	Presentations #3
11	P	Presentations #4
12	P	Presentations #5

Class types explained

L

Lecture; just sit back, pay attention, and ask questions when needed/useful; you can read the literature afterwards

S

Seminar; your active contribution is expected, prepare by reading the mandatory literature in advance

P

Student presentations; your active contribution is expected, but no need to prepare (unless you present, of course)

Literature

1. Wasserman, L. All of Statistics - A Concise Course in Statistics, Springer, 2004.
2. Ming, L & Vitányi, PMB. An Introduction to Kolmogorov Complexity and Its Applications (3rd ed), Springer, 2008.
3. Grünwald, P. The Minimum Description Length Principle, MIT Press, 2007.
4. Campana, BJL & Keogh, EJ. A Compression-Based Distance Measure for Texture. In: Proceedings of SIAM Data Mining (SDM'10), 2010.
5. Faloutsos, C & Megalooikonomou, V. On data mining, compression, and Kolmogorov complexity. In: Data Min. Knowl. Discov. 15(1):3-20, 2007.
6. Cilibrasi, R & Vitányi, PMB. Clustering by Compression. In: IEEE Transactions on Information Theory 15(4):1523-1545, 2005.
7. Vreeken, J, van Leeuwen, M & Siebes, A. Krimp: Mining Itemsets that Compress. In: Data Min. Knowl. Discov. 23(1):169-214, 2011.
8. van Leeuwen, M & Vreeken, J. Mining and Using Sets of Patterns through Compression. In CC Aggarwal & J Han, eds, Frequent Pattern Mining, Springer, 2014.
9. Budhathoki, K & Vreeken, J. The Difference and the Norm – Characterising Similarities and Differences between Databases. In: Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), pp 206-223, 2015.
10. van Leeuwen, M & Galbrun, E. Association Discovery in Two-View Data. Transactions on Knowledge and Data Engineering 27(12):3190-3202, 2015.
11. Tatti, N & Vreeken, J. The Long and the Short of It: Summarising Event Sequences with Serial Episodes. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp 462-470, 2012.
12. Koutra, D, Kang, U, Vreeken, J & Faloutsos, C. VoG: Summarizing and Understanding Large Graphs. In: Proceedings of the SIAM International Conference on Data Mining (SDM), pp 91-99, 2014.
13. Tatti, N & Vreeken, J. Finding Good Itemsets by Packing Data. In: Proceedings of the IEEE International Conference on Data Mining (ICDM), pp 588-597, 2008.
14. Smet, K & Vreeken, J. SLIM: Directly Mining Descriptive Patterns. In: Proceedings of the SIAM International Conference on Data Mining (SDM), pp 236-247, 2012.
15. Siebes, A & Kersten, R. A Structure Function for Transaction Data. In: Proceedings of the SIAM International Conference on Data Mining (SDM), pp 558-569, 2011.
16. De Bie, T. An Information Theoretic Framework for Data Mining. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp 564-572, 2011.
17. De Bie, T. Maximum entropy models and subjective interestingness: an application to tiles in binary databases. In: Data Min. Knowl. Discov. 23(3):407-446, 2011.
18. van Leeuwen, M, De Bie, T, Spyropoulou, E & Mesnage, C. Subjective Interestingness of Subgraph Patterns. In: Machine Learning 105(1):41-75, 2016.
19. Galbrun, E. The Minimum Description Length Principle for Pattern Mining: A Survey arXiv:2007.14009, 2021.