Agrupamiento

Lecturas recomendadas
Martin Ester, Hans-Peter Kriegel, Jörg Sander & Xiaowei Xu: A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. KDD 1996, pages 226-231.
Rakesh Agrawal, Johannes Gehrke, Dimitrios Gunopulos & Prabhakar Raghavan: Automatic Subspace Clustering of High Dimensional Data for Data Mining Applications. SIGMOD 1998, pages 94-105.
George Karypis, Eui-Hong (Sam) Han & Vipin Kumar: Chameleon: Hierarchical Clustering Using Dynamic Modeling, Computer, Vol. 32, No. 8, August 1999, pp. 68-75
Levent Ertöz, Michael Steinbach & Vipin Kumar: Finding Clusters of Different Sizes, Shapes, and Densities in Noisy, High Dimensional Data. Third SIAM International Conference on Data Mining, San Francisco, CA, USA, May 1-3. SDM 2003
Surveys
Anil K. Jain, M. Narasimha Murty & Patrick J. Flynn: Data Clustering: A Review. ACM Computing Surveys, Vol. 31, No. 3, pages 264-323, September 1999. DOI 10.1145/331499.331504
Lance Parsons, Ehtesham Haque & Huan Liu: Subspace Clustering for High Dimensional Data: A Review. ACM SIGKDD Explorations Newsletter, Volume 6, Issue 1, June 2004, pages 90-105. DOI 10.1145/1007730.1007731
Huan Liu, Farhad Hussain, Chew Lim Tan & Manoranjan Dash: Discretization: An Enabling Technique. Data Mining and Knowledge Discovery 6(4):393-423 (2002). DOI 10.1023/A:1016304305535
Publicaciones de IDBIS
Julio-Omar Palacio-Niño & Fernando Berzal: Evaluation Metrics for Unsupervised Learning Algorithms. arXix, 2019. https://arxiv.org/abs/1905.05667
Fernando Berzal, Juan Carlos Cubero, Nicolás Marín & Daniel Sánchez: Building multi-way decision trees with numerical attributes. Information Sciences, Volume 165, pages 73-90, 2004. DOI 10.1016/j.ins.2003.09.018