Clustering is a basic tool used in data analysis, pattern recognition and data mining for finding unknown groups in data. In this course, we learn algorithms for finding the location of clusters, and cluster validity measures to recognize how many clusters there are. Feature selection, data normalization and outlier removal are also considered.
Teacher: Pasi Fränti
Schedule: 28 h, starting from 9.1.
Monday 12-14 (D106)
Tuesday 12-14 (D106)
Schedule and Lecture Notes
Teachers: Niina Päivinen (Kuopio) and Qinpei Zhao (Joensuu)
Schedule: 16 h, starting from 12.1.
Thursday: 12-14 (B179)
Demo 1
Demo 2
Demo 3
Demo 4
Demo 5
Demo 6
Demo 7
Materials for the exercises and course work
Project presentations 1: 1.3. at 14-16 (Room 106 in Joensuu, MT3 in Kuopio)
Project presentations 2: 6.3. at 16-18 (Room 106 in Joensuu, MT3 in Kuopio)
Sufficient knowledge of Data structures and algorithms.
8.3. 14-16, Room 179 (Joensuu)
9.3. 12-16, Room MT2 (Kuopio)
Lecture notes
S. Theodoridis and K. Koutroumbas, Pattern Recognition, Academic Press, 3rd edition, 2006.
C. Bishop, Pattern Recognition and Machine Learning, Springer, 2006.
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