Type | Advanced Lecture (6 ECTS) |
Lecturer | Dr. Jilles Vreeken |
tada-staff (at) mpi-inf.mpg.de | |
Lectures |
Thursdays, 10–12 o'clock in Room E1.7 0.01. (and, a few times Tuesdays, 10–12 o'clock in Room E1.7 0.01) |
Summary | In this advanced course we'll be investigating hot topics in data mining that the lecturer thinks are cool. This course is for those of you who are interested in Data Mining, Machine Learning, Data Science, Big Data Analytics – or, as the lecturer prefers to call it – Algorithmic Data Analysis. We'll be looking into how to discover significant and useful patterns from data, efficiently measure non-linear correlations and determine causal relations, as well as how to analyse structured data such as time series and graphs. |
Month | Day | Topic | Slides | Assignment | Req. Reading | Opt. Reading |
---|---|---|---|---|---|---|
April | 11 | Jilles travelling – no class | ||||
18 | still travelling – no class | |||||
23* (Tue) | Introduction, Practicalities | 1st assignment out | ||||
25 | Interesting Patterns | [1] 4–4.2 | [9,10,11,12,13,14] | |||
30* (Tue) | Useful Patterns | [2,3] | [15,16] | |||
May | 2 | Jilles travelling – no class | deadline 1st, 2nd out | |||
9 | Actionable Patterns | [4] | [17,18,19] | |||
16 | Significance | [1] Sec 4.4, 5 | [20,21,22,23] | |||
23 | Dependence | [5] | [24,25,26] | |||
28* (Tue) | Causality | [27] Ch 1, 6.1, 6.3 | [27] Ch 6.5, 6.7 | |||
30 | yay holiday – no class | deadline 2nd, 3rd out | ||||
June | 6 | Causal Inference | [27] Ch 2.1, 4 | [28,29] | ||
13 | Jilles travelling – no class | |||||
18* (Tue) | Rumours in Graphs | [6] | [30,31,32,33] | |||
20 | yay holiday – no class | |||||
27 | Graph Summarization | [7] | [,35,36,37] | |||
July | 4 | It's not Fair! | deadline 3rd | [38] | ||
9 | One, Two,...Tensor! | [8] | [39,40] | |||
11 | Wrap-Up | |||||
17 | registration deadline exam | |||||
(24), 25, 26 | oral exams | |||||
September | 26 | re-exams |
* Tuesday at 10:00 till 12:00 in E1.7 Room 0.01
All report deadlines are on the indicated day at 10:00. In doubt, the assignment web page determines the exact date and time.
All required and optional reading will be made available. You will need a username and password to access the papers outside the MMCI/MPI network. Contact the lecturer if you don't know the username or password.
In case you do not have a strong enough background in data mining, machine learning, or statistics, these books may help to get you on your way [41,27,42] The university library kindly keeps hard copies of these books available in a so-called Semesteraparat.
[1] | Interesting Patterns. In Frequent Pattern Mining, Aggarwal, C. & Han, J., pages 105-134, Springer, 2014. |
[2] | Krimp: Mining Itemsets that Compress. Data Mining and Knowledge Discovery, 23(1):169-214, Springer, 2011. |
[3] | Mining and Using Sets of Patterns through Compression. In Frequent Pattern Mining, Aggarwal, C. & Han, J., pages 165-198, Springer, 2014. |
[4] | Subgroup Discovery. WIRE's Data Mining and Knowledge Discovery, 5:35-49, Wiley, 2015. |
[5] | New Evidence for the Theory of the Stork. Paediatric and Perinatal Epidemiology, 18(1):88-92, 2004. |
[6] | Spotting Culprits in Epidemics: How many and Which ones?. In Proceedings of the 12th IEEE International Conference on Data Mining (ICDM), Brussels, Belgium, IEEE, 2012. |
[7] | VoG: Summarizing Graphs using Rich Vocabularies. In Proceedings of the 14th SIAM International Conference on Data Mining (SDM), Philadelphia, PA, pages 91-99, SIAM, 2014. |
[8] | Tensor decompositions and applications. SIAM Rev., 51(3):455-500, 2009. |
[9] | Fast Discovery of Association Rules. In Advances in Knowledge Discovery and Data Mining, pages 307-328, AAAI/MIT Press, 1996. |
[10] | Efficiently mining long patterns from databases. In Proceedings of the ACM International Conference on Management of Data (SIGMOD), Seattle, WA, pages 85-93, 1998. |
[11] | Discovering Frequent Closed Itemsets for Association Rules. In Proceedings of the 7th International Conference on Database Theory (ICDT), Jerusalem, Israel, pages 398-416, ACM, 1999. |
[12] | Self-sufficient itemsets: An approach to screening potentially interesting associations between items. ACM Transactions on Knowledge Discovery from Data, 4(1):1-20, 2010. |
[13] | Tiling Databases. In Proceedings of Discovery Science, pages 278-289, 2004. |
[14] | Comparing Apples and Oranges: Measuring Differences between Data Mining Results. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), Athens, Greece, pages 398-413, Springer, 2011. |
[15] | Identifying the Components. Data Mining and Knowledge Discovery, 19(2):173-292, 2009. |
[16] | The Odd One Out: Identifying and Characterising Anomalies. In Proceedings of the 11th SIAM International Conference on Data Mining (SDM), Mesa, AZ, pages 804-815, Society for Industrial and Applied Mathematics (SIAM), 2011. |
[17] | Bump Hunting in High-Dimensional Data. Statistics and Computing, 9:123-143, Springer, 1999. |
[18] | Identifying Consistent Statements about Numerical Data with Dispersion-Corrected Subgroup Discovery. Data Mining and Knowledge Discovery, 31(5):1391-1418, Springer, 2017. |
[19] | Efficiently Discovering Locally Exceptional yet Globally Representative Subgroups. In Proceedings of the 17th IEEE International Conference on Data Mining (ICDM), New Orleans, LA, IEEE, 2017. |
[20] | Assessing data mining results via swap randomization. ACM Transactions on Knowledge Discovery from Data, 1(3), ACM, 2007. |
[21] | Tell me something I don't know: randomization strategies for iterative data mining. In Proceedings of the 15th ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD), Paris, France, pages 379-388, ACM, 2009. |
[22] | On the rationale of maximum-entropy methods. Proceedings of the IEEE, 70(9):939-952, IEEE, 1982. |
[23] | Maximum entropy models and subjective interestingness: an application to tiles in binary databases. Data Mining and Knowledge Discovery, 23(3):407-446, Springer, 2011. |
[24] | Detecting Novel Associations in Large Data Sets. Science, 334(6062):1518-1524, 2011. |
[25] | On cumulative entropies. Journal of Statistical Planning and Inference, 139(2009):4072-4087, 2009. |
[26] | Multivariate Maximal Correlation Analysis. In Proceedings of the 31st International Conference on Machine Learning (ICML), Beijing, China, pages 775-783, JMLR, 2014. |
[27] | Elements of Causal Inference. MIT Press, 2017. |
[28] | Causal Inference Using the Algorithmic Markov Condition. IEEE Transactions on Information Technology, 56(10):5168-5194, 2010. |
[29] | Information-geometric Approach to Inferring Causal Directions. , 182-183:1-31, 2012. |
[30] | Finding effectors in social networks. In Proceedings of the 16th ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD), Washington, DC, pages 1059-1068, ACM, 2010. |
[31] | Rumors in a Network: Who's the Culprit?. IEEE Transactions on Information Technology, 57(8):5163-5181, 2011. |
[32] | Hidden Hazards: Finding Missing Nodes in Large Graph Epidemics. In Proceedings of the SIAM International Conference on Data Mining (SDM'15), SIAM, 2015. |
[33] | Reconstructing an Epidemic over Time. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'16), pages 1835-1844, ACM, 2016. |
[] | Fully automatic cross-associations. In Proceedings of the 10th ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD), Seattle, WA, pages 79-88, 2004. |
[35] | Beyond Caveman Communities: Hubs and Spokes for Graph Compression and Mining. In Proceedings of the 11th IEEE International Conference on Data Mining (ICDM), Vancouver, Canada, pages 300-309, IEEE, 2011. |
[36] | TimeCrunch: Interpretable Dynamic Graph Summarization. , pages 1055-1064, 2015. |
[37] | MeGS: Partitioning Meaningful Subgraph Structures Using Minimum Description Length. In Proceedings of the IEEE International Conference on Data Mining (ICDM), pages 889-894, IEEE, 2016. |
[38] | Fairness through Awareness. In Proceedings of the 3rd Innovations in Theoretical Computer Science Conference (ITCS), ACM, 2012. |
[39] | Understanding Complex Datasets: Data Mining with Matrix Decompositions. Chapman \& Hall/CRC, 2007. |
[40] | Unsupervised Multiway Data Analysis: A Literature Survey. IEEE Trans. Knowl. Data En., 21(1):6-20, 2009. |
[41] | Data Mining - The Textbook. Springer, 2015. |
[42] | All of Statistics. Springer, 2005. |
The course has two hours of lectures per week. There are no weekly tutorial group meetings. Instead, you will have to write four essays based on the material covered on the lectures and scientific articles assigned by the lecturer.
In general terms, the course will consist of
Students will individually do one assignment per topic – four in total. For every assignment, you will have to read one or more research papers and hand in a report that critically discusses this material and answers the assignment questions. Reports should summarise the key aspects, but more importantly, should include original and critical thought that show you have acquired a meta level understanding of the topic – plain summaries will not suffice. All sources you've drawn from should be referenced. The expected length of a report is 3 pages, but there is no limit.
A sample assignment from 2015, together with a well-graded report can be found here.
The deadlines for the reports are on the day indicated in the schedule at 10:00 Saarbrücken standard-time. You are free to hand in earlier.
The assignments will be graded in scale of Fail, Pass, Good, and Excellent. Any assignment not handed in by the deadline is automatically considered failed, and cannot be re-done. You are allowed to re-do one Failed assignment: you have to hand in the improved assignment within two weeks. Two failures mean you are not eligible for the exam, and hence failed the course.
You can earn up to three bonus points by obtaining Excellent or Good grades for the assignments. An Excellent grade gives you one bonus point, as do every two Good grades, up to a maximum of three bonus points. Each bonus point improves your final grade by 1/3 assuming you pass the final exam. For example, if you have two bonus points and you receive 2.0 from the final exam, your final grade will be 1.3. You fail the course if you fail the final exam, irrespective of your possible bonus points. Failed assignments do not reduce your final grade, provided you are eligible to sit the final exam.
The final exams will be oral. The final exam will cover all the material discussed in the lectures and the topics on which you did your assignments. The main exam will be on July 25th. The re-exam will be on September 26th. The exact time slot per student will be announced per email. Inform the lecturer of any potential clashes as soon as you know them.
Students should have basic working knowledge of data analysis and statistics, e.g. by successfully having taken courses related to data mining, machine learning, and/or statistics, such as Information Retrieval and Data Mining, Machine Learning, Probabilistic Graphical Models, Statistical Learning, etc.