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Veranstaltungsbeschreibung

113444a Data Mining und Mustererkennung

Zuletzt geändert:18.03.2013 / Maucher
EDV-Nr:113444a
Studiengänge: Medieninformatik (Bachelor, 7 Semester), Prüfungsleistung im Modul Data Mining in Semester 3 4 6 7
Häufigkeit: nie
Dozent: Prof. Dr. Johannes MaucherDetails zum Dozenten
Sprache: Deutsch
Art: V
Umfang: 4 SWS
ECTS-Punkte: 4
Workload: Versuchstermine
8 Termine zu je 4 SWS = 24 Zeitstunden
Vor- und Nachbearbeitung der Versuche
8 Termine zu je 8 SWS = 48 Zeitstunden
Einführungsveranstaltungen
5 Termine zu je 4 SWS=15 Zeitstunden
Vor- und Nachbearbeitung der Einführungsveranstaltungen
5 Termine zu je 8 SWS =30 Zeitstunden
Summe: 117 Zeitstunden
Prüfungsform:
Beschreibung: In this course 6 different data mining and pattern recognition applications are implemented by all student groups. A group contains at most 3 students. The implementation of each application should be done within one afternoon (14.15h-17.30h).
The applications are:
Data Mining Process: In this exercise the successive steps of the Data Mining process in general are demonstrated. The Open Source Data Mining toll Weka is applied for supervised and unsupervised learning algorithms, for data preprocessing routines and for performance evaluation.

Recommender Systems: Recommender Systems are applied in E-commerce for generating customized recommendations. Well known are the Amazon.com recommendations which are either distributed by e-mail or presented on the Amazon web page after login. For generating these recommendations the products which have already purchased or reviewed by the user are taken into account. In this exercise the currently most popular algorithms (Collaborative Filtering) for generating recommendations are implemented, tested and analysed.

Mining Data from Amazon.com: Using the Amazon Web Service (AWS) one can access loads of product and review data from Amazon.com. In this exercise we integrate the data in our programms using a python wrapper for AWS. Then we apply various intelligent algorithms for mining interesting knowledge out of this data. E.g. we perform trend analysis or predict price models. Students are free to develop their own data mining applications.

Spam Filter: A Naive Bayes Classifier is implemented for filtering spam. It is also shown how to apply this algorithm for document classification in general

Document Clustering: In this excercise a large amount of RSS-Newsfeeds is collected. All articles coming from the different feeds are clustered using non-negative matrix factorisation. Essential features of each document cluster are extracted

Face detectionThe eigenface-approach for face recognition is implemented and tested in thies exercise

All applications are implemented in Python.
In addition, students have to prepare presentations on the 6 different applications. Each student group selects one application and presents the theory and background of the applications to all other students. These presentations are scheduled before the start of the first practical excercise.
Literatur:
  • Toby Segaran; Programming Collective Intelligence; O'Reily Verlag
  • I.H. Witten, E. Frank; Data Mining; Morgan Kaufmann Verlag


Weitere Literatur finden Sie in der HdM-Bibliothek.
Internet: http://www.hdm-stuttgart.de/~maucher/
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