Note

Achieving at least 50% of the total homework points is advisable.

Please drop your solutions into the silver homework box located at the second floor of Informatikzentrum at the Information Systems Institute (in front of the elevators) until Tuesday, before the next lecture (the date is mentioned on each exercise sheet). You may answer in either German or English. You are encour-aged to work in teams of 2 students (not more than 2), and send your solution as a team. Please mention in your email the name of both students together with the corresponding inmatriculation numbers (“Matrikelnummer”).

ITIS students outside Braunschweig can send their solutions per mail at silviuifis [dot] cs [dot] tu-bs [dot] de

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Data Mining Literature:

The following paper presents an overview of the main techniques we have discussed in the data mining part of the lecture, and should be used as a starting point for each algorithm. Please read the citations of each technique for more speciffic information:

X. Wu, V. Kumar, J. Quinlan, J. Ghosh, Q. Yang, H. Motoda, G. McLachlan, A. Ng, B. Liu, P. Yu, Z. Zhou, M. Steinbach, D. Hand, D. Steinberg. Top 10 Algorithms in Data Mining.

Journal Knowledge and Information Systems archive, Volume 14 Issue 1. [http]

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