CCO - 725 - Unsupervised and Semi Supervised Learning – Clustering Algorithms

Total of Credits: 8
Hours for Theoretical Classes: 60
Hours for Exercises or Seminars: 60

Objective

To present a comprehensive view of unsupervised and semi supervised learning, by means of the study of clustering algorithms of varied types, hard and fuzzy. Similarity measures and clustering evaluation metrics are also studied.

Catalog Description

  • Introduction – basic notions of machine learning, inductive learning, unsupervised and semi supervised learning
  • Basic Concepts – similarity, disimilarity, types of attributes
  • Particional Clustering – hard and fuzzy algorithms
  • Hierarchical Clustering
  • Semi supervised Clustering – hard and fuzzy algorithms
  • Clustering Evaluation Metrics
  • Other Clustering Approaches 

Main Bibliography

  1. THEODORIDIS, Sergios, 1951; KOUTROMBAS, Konstantinos. Pattern recognition. 4 ed. Burlington: Elsevier, c2009. 961 p. ISBN 978-1-59749-272-0.
  2. DUDA, Richard O.; HART, Peter E.; STORK, David G.. Pattern classification. 2 ed. New York: John Wiley & Sons, c2001. 654 p.
  3. BISHOP, Christopher M.. Pattern recognition and machine learning. New York: Springer, c2006. 738 p. -- (Information Science and Statistics) ISBN 978-0-387-31073-2.
  4. PAL, Sankar K.; MITRA, Pabitra. Pattern recognition algorithms for data mining: scalability, knowledge discovery, and soft granular computing. Boca Raton: CRC Press, c2004. 244 p. ISBN 1-58488-457-6.
  5. Pedrycz, Witold – Knowledge-based clustering: from data to information granules, Wiley, 2005, 316 p. ISBN 0-471-46966-1.
  6. JAIN, A. K.; Murty, M. N.; FLYNN, P. J. Data Clustering: A Review. ACM Computing Surveys, vol. 31, N. 3, pp. 264-323, 1999.

Complementary Bibliography

  1. JAIN, A. K. Data Clustering: 50 years beyond K-means. Pattern Recognition Letters, vol. 31, n. 8, pp. 651-666, 2010.
  2. Basu, S.; Banerjee, A.; Mooney, R. J., 2002. Semi-supervised clustering by seeding. In: ICML ’02: Proceedings of the Nineteenth International Conference on Machine Learning p. 27–34.
  3. Basu, S.; Banerjee, A.; Mooney, R. J., 2004. Active semi-supervision for pairwise constrained clustering. In: In Proceedings of the 2004 SIAM International Conference on Data Mining p. 333–344.
  4. Wagstaff, K.; Cardie, C.; Rogers, S.; Schrödl, S., 2001. Constrained k-means clustering with background knowledge. In: ICML ’01: Proceedings of the Eighteenth International Conference on Machine Learning p. 577–584: Morgan Kaufmann Publishers Inc.
  5. Grira, N.; Crucianu, M.; Boujemaa, N. Active semi-supervised fuzzy clustering. Pattern Recognition. v. 41, n. 5, p. 1851–1861, 2008.