Mixmod : a software package for data supervised and unsupervised classification

This software was developed (or is under development) within the higher education and research community. Its stability can vary (see fields below) and its working state is not guaranteed.
Higher Edu - Research dev card
  • Creation or important update: 26/10/10
  • Minor correction: 17/01/13
  • Index card author: Florent Langrognet (Laboratoire de Mathématiques de Besancon)
  • Theme leader: Teresa Gomez-Diaz (LIGM)
Keywords
General software features
  • Supervised Classification
  • Unsupervised Classification

for quantitive and qualitative data.

To address issues of data classification, Mixmod uses mixture models, powerful and flexible tool.

Among the many features and tools available in Mixmod:

  • Processing quantitative data (with Gaussian mixture models) or qualititative (with Multinomial mixture models)
  • Parsimonious mixture models
  • Specific models for the treatment of high-dimensional data (individuals characterized by a large number of features)
  • EM, CEM, SEM Algorithms
  • Many strategies initialization
  • Criteria for selection of models and the number of classes suited to different purposes
Context in which the software is used

The Mixmod software package consists :

  • a library (C++): mixmodLib
  • a Graphical User Interface: mixmodGUI developed with QT : mixmodGUI
  • functions for R: RMixmod
  • functions for Matlab: mixmodForMatlab
Publications related to software
  •  "MIXMOD : un logiciel de classification supervisée et non supervisée pour données quantitatives et qualitatives",
    F. Langrognet,
    La Revue de Modulad, numéro 40 (2009)
  •  "Le logiciel MIXMOD d'analyse de mélange pour la classification et l'analyse discriminante"
    C. Biernacki, G. Celeux, A. Echenim, G. Govaert, F. Langrognet,
    La Revue de Modulad 35, pp. 25-44. (2007)
  • "Model-Based Cluster and Discriminant Analysis with the MIXMOD Software",
    C. Biernacki, G. Celeux, G. Govaert, F. Langrognet,
    Computational Statistics and Data Analysis, vol. 51/2, pp. 587-600. (2006)