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.