TDDD41 / 732A61 Data Mining - Clustering and Association Analysis
Course literatureThe course literature consists of a course book as well as articles (see below) and lab assignment descriptions. The course book is:
- Jiawei Han, Micheline Kamber, Data Mining - Concepts and Techniques, 2nd edition, Morgan-Kaufmann, 2006. ISBN: 978-1-55860-901-3
Jiawei Han, Micheline Kamber, Jian Pei, Data Mining - Concepts and Techniques, 3rd edition, Morgan-Kaufmann, 2011. ISBN: 978-0123814791
- Data mining introduction
- course book 2nd edition: 1.1-6, 2.1 / course book 3rd edition: 1.1-6, 3.1
- Clustering - introduction and distances for different types of data objects
- course book 2nd edition: 7.1-3 / course book 3rd edition: 2.1, 2.4, 10.1
- Clustering - Partitioning Methods
- course book 2nd edition: 7.4 / course book 3rd edition: 10.2
- Raymond T Ng, Jiawei Han. Efficient and Effective Clustering Methods for Spatial Data Mining, VLDB 94, 144--155, 1994. (CLARANS, also introduction to PAM and CLARA)
- Clustering - Hierarchical Methods
- course book 2nd edition: 7.5 / course book 3rd edition: 10.3 (except 10.3.5) (Obs: ROCK not in 3rd edition)
- Tian Zhang, Raghu Ramakrishnan, and Miron Livny. BIRCH : an efficient data clustering method for very large databases. SIGMOD 96, 103-114, 1996.
- Sudipto Guha, Rejeev Rastogi, and Kyuseok Shim. ROCK: A robust clustering algorithm for categorical attributes, Information Systems 25(5):345-366, 2000.
- George Karypis, Eui-Hong Han, and Vipin Kumar. CHAMELEON: A Hierarchical Clustering Algorithm Using Dynamic Modeling, COMPUTER 32(8): 68-75, 1999.
- Clustering - Density-Based Methods
- course book 2nd edition: 7.6 / course book 3rd edition: 10.4
- Mihael Ankerst, Markus M Breunig, Hans-Peter Kriegel, Jörg Sander. Optics: Ordering points to identify the clustering structure, SIGMOD 99, 49-60, 1999. (also introduction to DBSCAN)
- Alexander Hinneburg, Daniel A. Keim. An Efficient Approach to Clustering in Large Multimedia Databases with Noise, KDD 98, 58-65, 1998. (DENCLUE)
- Association analysis - introduction
- course book 2nd edition: 5.1 / course book 3rd edition: 6.1
- Association analysis - Apriori algorithm
- course book 2nd edition: 5.2.1-2, 5.4 / course book 3rd edition: 6.2.1-2, 6.4
- R. Agrawal and R. Srikant. Fast Algorithms for Mining Association Rules, Proc. of the 20th Int. Conf. on Very Large Databases, Santiago, Chile, September 1994. Expanded version available as IBM Research Report RJ9839, June 1994.
- Association analysis - FP grow algorithm
- course book 2nd edition: 5.2.4 / course book 3rd edition: 6.2.4
- J. Han, J. Pei, and Y. Yin. Mining Frequent Patterns without Candidate Generation, Proc. 2000 ACM-SIGMOD Int. Conf. on Management of Data (SIGMOD'00), Dallas, TX, May 2000.
- Association analysis - Constraints
- course book 2nd edition: 5.5 / course book 3rd edition: 7.3
- J. Pei and J. Han. Can We Push More Constraints into Frequent Pattern Mining?, Proc. 2000 Int. Conf. on Knowledge Discovery and Data Mining (KDD'00), Boston, MA, August 2000.
Page responsible: Patrick Lambrix
Last updated: 2017-01-25