BIBLIOGRAFIA
C. C. Aggarwal, P. S. Yu, Redefining Clustering for High-Dimensional Applications.
C. Ordonez, E. Omiecinski, N. Ezquerra, A Fast Algorithm to Cluster High Dimensional Basket Data.
D. Dorbolò, G. Frosini, B. Lazzerini, Programmazione a oggetti con riferimento al C++, Milano, Franco Angeli, 2000.
A. Domenici, G. Frosini, Introduzione alla programmazione ed elementi di strutture dati con il linguaggio C++, Milano, Franco Angeli, 1996.
C. Savy, Da C++ a UML : guida alla progettazione, Milano, McGraw-Hill, 2000.
P. Giudici, Data mining - Metodi statistici per le applicazioni aziendali, McGraw-Hill, 2001.
C. Ordonez, E. Omiecinski, FREM: Fast and Robust EM Clustering for Large Data Sets.
C. Ordonez, Clustering Binary Data Streams with K-means.
R. M. Neal, G. E. Hinton, A view of the EM algorithm that justifies incremental, sparse, and other variants.
B. Thiesson, C. Meek, D. Heckerman, Accelerating EM for Large Databases.
Z. Ghahramani, M. I. Jordan, Learning from incomplete data.
M. Welling, M. Weber, A Constrained EM Algorithm for Independent Component Analysis.
Y. Ivanov, B. Blumberg, A. Pentland, Expectation Maximization for Weakly Labeled Data.
S. McClean, B. Scotney, K. Greer, R. Páircéir, Conceptual Clustering of Heterogeneous Distributed Databases.
S. Ng, G. J. McLachlan, On some Variants of the EM Algorithm for the Fitting of Finite Mixture Models.
K. Tsuda, S. Akaho, K. Asai, The em Algorithm for Kernel Matrix Completion with Auxiliary Data.
J. J. Verbeek, N. Vlassis, J. R. J. Nunnink, A Variational EM Algorithm for Large-Scale Mixture Modeling.
V. A. Manganaro, Introduzione al KDD e al DATA MINING.