N
Nizar Bouguila
Researcher at Concordia University
Publications - 514
Citations - 7257
Nizar Bouguila is an academic researcher from Concordia University. The author has contributed to research in topics: Mixture model & Dirichlet distribution. The author has an hindex of 44, co-authored 435 publications receiving 5849 citations. Previous affiliations of Nizar Bouguila include Université de Sherbrooke & Concordia University Wisconsin.
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Journal ArticleDOI
Unsupervised learning of a finite mixture model based on the Dirichlet distribution and its application
TL;DR: An unsupervised algorithm for learning a finite mixture model from multivariate data based on the Dirichlet distribution, which offers high flexibility for modeling data.
Journal ArticleDOI
High-Dimensional Unsupervised Selection and Estimation of a Finite Generalized Dirichlet Mixture Model Based on Minimum Message Length
Nizar Bouguila,Djemel Ziou +1 more
TL;DR: This work considers the application of the minimum message length (MML) principle to determine the number of clusters in a finite mixture model based on the generalized Dirichlet distribution.
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A Hybrid Feature Extraction Selection Approach for High-Dimensional Non-Gaussian Data Clustering
TL;DR: An unsupervised approach for feature selection and extraction in mixtures of generalized Dirichlet (GD) distributions that is able to extract independent and non-Gaussian features without loss of accuracy is presented.
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Unsupervised selection of a finite Dirichlet mixture model: an MML-based approach
Nizar Bouguila,Djemel Ziou +1 more
TL;DR: The minimum message length (MML) principle is extended to determine the number of clusters in the case of Dirichlet mixtures and the resulting method is validated for one-dimensional and multidimensional data.
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A Fast Clustering Algorithm based on pruning unnecessary distance computations in DBSCAN for High-Dimensional Data
TL;DR: Theoretical analysis and experimental results show that NQ-DBSCAN averagely runs in O(n*log(n) with the help of indexing technique, and the best case is O( n) if proper parameters are used, which makes it suitable for many realtime data.