Article citationsMore>>

Chi, J., Yuchen, Z., Sivaraman, B., Martin, J. and Michael, I. (2016) Local Maxima in the Likelihood of Gaussian Mixture Models: Structural Results and Algorithmic Consequences. Advances in Neural Information Processing Systems, Barcelona, 5-10 December 2016, 4116-4124.

has been cited by the following article:

• TITLE: Modeling Methods in Clustering Analysis for Time Series Data

  AUTHORS: Naglaa A. Morad

  KEYWORDS: Gaussian Mixture Model-Based Clustering (GMMC), The Expectation-Maximization (EM) Algorithm, AIC, SBC, ICOMP PEU

  JOURNAL NAME: Open Journal of Statistics, Vol.10 No.3, June 30, 2020

  ABSTRACT: This paper is concerned about studying modeling-based methods in cluster analysis to classify data elements into clusters and thus dealing with time series in view of this classification to choose the appropriate mixed model. The mixture-model cluster analysis technique under different covariance structures of the component densities is presented. This model is used to capture the compactness, orientation, shape, and the volume of component clusters in one expert system to handle Gaussian high dimensional heterogeneous data set. To achieve flexibility in currently practiced cluster analysis techniques. The Expectation-Maximization (EM) algorithm is considered to estimate the parameter of the covariance matrix. To judge the goodness of the models, some criteria are used. These criteria are for the covariance matrix produced by the simulation. These models have not been tackled in previous studies. The results showed the superiority criterion ICOMP PEU to other criteria. This is in addition to the success of the model based on Gaussian clusters in the prediction by using covariance matrices used in this study. The study also found the possibility of determining the optimal number of clusters by choosing the number of clusters corresponding to lower values for the different criteria used in the study.