# Seminar aus Künstlicher Intelligenz

The Seminar is available in Tucan right here.

Jilles Vreeken will give a talk about "Mining Sequential Patterns" on Thursday, Dec. 1, 2016 from 13:30 - 14:30 in C110

### When and where?

The seminar meetings will be on **Tuesdays **at** 17:10h** in Room **S2 02/E202** (Piloty building).

The **kick-off meeting** is on Tuesday, **October 25.**

### Organisation

The topics for the talks will be assigned in the kick-off meeting. The language of the seminar is English.

It is not necessary to have prior knowledge in artificial intelligence, but prior knowledge in data mining and machine learning is helpful. Participation is limited to 20 students. In case we have more students, students with prior knowledge in data mining and knowledge discovery will be preferred. The selection will be made at kick-off meeting.

For further questions feel free to send an email to ml-sem@ke.tu-darmstadt.de. No prior registration is needed, however, please stlll send us an email so that we are able to estimate beforehand the number of participants, and have your E-mail address for possible announcements. Also make sure that you are registered in TUCaN.

### Content

In the course of this seminar we will try to get an overview on the current state of research in a domain. This year's topic will be **Time Series and Event Sequences**, i.e. methods and approaches for handling problems where the data have a temporal order. We will concentrate on recent papers published in workshops, journals, and conferences.

The students are expected to give a 30 minute talk on the material they are assigned, followed by 15 minutes of questions. Although each topic is typically associated with a single paper, the point of the talk is **not** to exactly reproduce the entire contents of the paper, but to communicate the key ideas of the methods that are introduced in the paper. Thus, the content of the talk should exceed the scope of the paper, and demonstrate that a thorough understanding of the material was achieved. See also our general advices on giving talks.

### Talks

The talks are expected to be accompanied by slides. In case you do not own a laptop, please send us the slides in advance, so that we can prepare and test the slides. The talk and the slides should be in English.Schedule

There will be two talks in each meeting. As mentioned above, each topic is associated with one paper, but the talk should not exactly reproduce the content of the paper, but communicate the key ideas of the introduced method.

All papers should be available on the internet or in the ULB. Note that Springer link often only works on campus networks (sometimes not even via VPN). If you cannot find a paper, contact us.

The planned schedule is as follows:

*15.11.2016:*

**Dynamic Time Warping**

*Tobias G.*(Slides)

Donald J. Berndt, James Clifford: Finding Patterns in Time Series: A Dynamic Programming Approach.*Advances in Knowledge Discovery and Data Mining*1996: 229-248.*Patryk H.*(Slides)

Michail Vlachos, Marios Hadjieleftheriou, Dimitrios Gunopulos, Eamonn J. Keogh: Indexing Multidimensional Time-Series.*VLDB Journal*15(1): 1-20 (2006)

*22.11.2016:*

**O**

**utlier Detection and Dimensionality Reduction in Time Series**

*Patrick K.*(Slides)

Eamonn J. Keogh, Kaushik Chakrabarti, Michael J. Pazzani, Sharad Mehrotra: Dimensionality Reduction for Fast Similarity Search in Large Time Series Databases. Knowl. Inf. Syst. 3(3): 263-286 (2001)*Jakob B.*(Slides)

Michael Jones, Daniel Nikovski, Makoto Imamura, Takahisa Hirata: Exemplar learning for extremely efficient anomaly detection in real-valued time series.*Data Mining and Knowledge Discovery*30(6): 1427-1454 (2016)

*29.11.2016:***Time Series Classification - Shapelets**

*Tomasz G.*(Slides)

L. Ye and E. Keogh. Time series shapelets: a novel technique that allows accurate, interpretable and fast classification.*Data Mining and Knowledge Discovery*, 22(1-2):149-182, 2011.*Marten P.*(Slides)

J. Hills, J. Lines, E. Baranauskas, J. Mapp, and A. Bagnall. Classification of time series by shapelet transformation.*Data Mining and Knowledge Discovery*, 28(4):851-881, 2014.

*06.12.2016:*

**Time Series Classification**

*Miriam M.*(Slides)

Patrick Schäfer: The BOSS is concerned with time series classification in the presence of noise.*Data Mining and Knowledge Discovery*29(6): 1505-1530 (2015)*Patrick R.*(Slides)

Rohit J. Kate: Using dynamic time warping distances as features for improved time series classification.*Data Mining and Knowledge Discovery*30(2): 283-312 (2016)

*13.12.2016:*

**Time Series Clustering**

*Sven K.*(Slides)

Anthony J. Bagnall, Gareth J. Janacek. Clustering Time Series with Clipped Data.*Machine Learning*58(2-3): 151-178 (2005)*Imed B.*(Slides)

Pedro Pereira Rodrigues, João Gama, João Pedro Pedroso: Hierarchical Clustering of Time-Series Data Streams.*IEEE Transactions on Knowledge and Data Engineering*20(5): 615-627 (2008)

*20.12.2016:***Sequence Modeling**

*Oliver S.*(Slides)

Lawrence R. Rabiner, Biing-Hwang Juang: An Introduction to Hidden Markov Models. In: IEEE. ASSP Magazine. Bd. 3, Nr. 1, Januar 1986, S. 4–16.*Jonas F.*(slides)

John D. Lafferty, Andrew McCallum, Fernando C. N. Pereira: Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data. Proceedings ICML 2001: 282-289

*10.01.2017:*

**Sequence Mining 1**

*Tümer T.*(Slides)

Ramakrishnan Srikant, Rakesh Agrawal: Mining Sequential Patterns: Generalizations and Performance Improvements.*Proceedings EDBT 1996*: 3-17#*Stefan H.*(Slides, Demo)

Mohammed Javeed Zaki: SPADE: An Efficient Algorithm for Mining Frequent Sequences.*Machine Learning*42(1/2): 31-60 (2001)

*17.01.2017:*

**Sequence Mining 2**

*Max L.*(Slides)

Minos N. Garofalakis, Rajeev Rastogi, Kyuseok Shim: Mining Sequential Patterns with Regular Expression Constraints.*IEEE Transactions on Knowledge and Data Engineering*14(3): 530-552 (2002)*Ruben G.*(Slides)

Jian Pei, Jiawei Han, Behzad Mortazavi-Asl, Jianyong Wang, Helen Pinto, Qiming Chen, Umeshwar Dayal, Meichun Hsu: Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach.*IEEE Transactions on Knowledge and Data Engineering*16(11): 1424-1440 (2004)

*24.01.2017:*

**Kein Seminar!**

*31.01.2017:*

**Sequence Classification**

*Alexander S.*(Slides)

Neal Lesh, Mohammed Javeed Zaki, Mitsunori Ogihara: Scalable Feature Mining for Sequential Data.*IEEE Intelligent Systems*15(2): 48-56 (2000)*Felix E.*(Slides)

Manuele Bicego, Vittorio Murino, Mário A. T. Figueiredo: Similarity-based classification of sequences using hidden Markov models.*Pattern Recognition*37(12): 2281-2291 (2004)

*07.02.2017:***Deep Neural Networks for Sequence Learning**

*Viktor P.*(Slides)

Ilya Sutskever, Oriol Vinyals, Quoc V. Le: Sequence to Sequence Learning with Neural Networks.*Proceedings NIPS 2014*: 3104-3112*Simon L.*(Slides)

Eck, D., & Schmidhuber, J. (2002). Finding temporal structure in music: Blues improvisation with LSTM recurrent networks. In*Proc. 12th IEEE Workshop on Neural Networks for Signal Processing*, 2002 (pp. 747-756). IEEE.

### Grading

**guidelines for giving a talk**.