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Evaluate the number of clusters in finite mixture models with the penalized histogram difference criterion
- Source :
- Journal of Process Control. 23:1052-1062
- Publication Year :
- 2013
- Publisher :
- Elsevier BV, 2013.
-
Abstract
- Aimed at the determination of the number of mixtures for finite mixture models (FMMs), in this work, a new method called the penalized histogram difference criterion (PHDC) is proposed and evaluated with other criteria such as Akaike information criterion (AIC), the minimum message length (MML), the information complexity (ICOMP) and the evidence of data criterion (EDC). The new method, which calculates the penalized histogram difference between the data generated from estimated FMMs and those for modeling purpose, turns out to be better than others for data with complicate mixtures patterns. It is demonstrated in this work that the PHDC can determine the optimal number of clusters of the FMM. Furthermore, the estimated FMMs asymptotically approximate the true model. The utility of the new method is demonstrated through synthetic data sets analysis and the batch-wise comparison of citric acid fermentation processes.
- Subjects :
- Finite mixture
business.industry
Pattern recognition
Information Criteria
Industrial and Manufacturing Engineering
Synthetic data sets
Computer Science Applications
Minimum message length
Control and Systems Engineering
Modeling and Simulation
Histogram
Information complexity
Expectation–maximization algorithm
Artificial intelligence
Akaike information criterion
business
Algorithm
Mathematics
Subjects
Details
- ISSN :
- 09591524
- Volume :
- 23
- Database :
- OpenAIRE
- Journal :
- Journal of Process Control
- Accession number :
- edsair.doi...........ac52d99cf366efbecd7a53d925ca70be
- Full Text :
- https://doi.org/10.1016/j.jprocont.2013.06.008