SUBJECT

Title

Data mining

Type of instruction

lecture + practical

Level

master

Faculty

Faculty of Science

Part of degree program

Mathematics MSc

Recommended in

Semesters 1-4

Typically offered in

Autumn/Spring semester

Course description

Basic concepts and methodology of knowledge discovery in databases and data mining. Frequent pattern mining, association rules. Level-wise algorithms, APRIORI. Partitioning and Toivonen algorithms. Pattern growth methods, FP-growth. Hierarchical association rules. Constraints handling. Correlation search.
Dimension reduction. Spectral methods, low-rank matrix approximation. Singular value decomposition. Fingerprints, fingerprint based similarity search.
Classification. Decision trees. Neural networks. k-NN, Bayesian methods, kernel methods, SVM.
Clustering. Partitioning algorithms, k-means. Hierarchical algorithms. Density and link based clustering, DBSCAN, OPTICS. Spectral clustering.
Applications and implementation problems. Systems architecture in data mining. Data structures.

Readings

Jiawei Han és Micheline Kamber: Data Mining: Concepts and Techniques, Morgan Kaufmann Publishers, 2000, ISBN 1558604898,

Pang-Ning Tan, Michael Steinbach, Vipin Kumar: Introduction to Data Mining, Addison-Wesley, 2006, ISBN 0321321367.

T. Hastie, R. Tibshirani, J. H. Friedman: The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer-Verlag, 2001.