[Lecture slides in PDF] [Lecture slides in PPT]

General survey

• Jain, A., M. Murty, and P. Flynn (1999). Data clustering: A review.

Comparing clustering

• Rand index
• Hubert, L., & Arabie, P. (1985). Comparing partitions..
• Meila, M. (2005). Comparing clusterings: an axiomatic view.

Dissimilarity function

• Read chapter 8.2 of Data Mining: Concepts and Techniques by J. Han and M. Kamber for dealing with different types of variables
• Eric P. Xing, Andrew Y. Ng, Michael I. Jordan, Stuart Russell (2002). Distance Metric Learning, with Application to Clustering with Side-information.

Estimating the number of clusters

• Trevor Hastie, Robert Tibshirani and Guenther Walther (2000) Estimating the number of data clusters visa the Gap Statistic

KD-tree for K-Means Clustering

• T. Kanungo, D. M. Mount, N. Netanyahu, C. Piatko, R. Silverman, and A. Y. Wu (2002). An efficient k-means clustering algorithm: Analysis and implementation

Spectral Clustering

• Jianbo Shi and Jitendra Malik (2000). Normalized Cuts and Image Segmentation. Software
• Marina Maila and Jianbo Shi (2001) Learning Segmentation with Random Walk
• A.Y.Ng, M.I.Jordan, and Y.Weiss (2002) On spectral clustering: Analysis and an algorithm.
• Ulrike von Luxburg (2006) A tutorial on Spectral Clustering