different clusters and generate meaningful interpretations.

most popularly used estimation approach for the mixture model

m is the expectation-maximisation (EM) algorithm [Dempster,

77], which is a two-step iterative procedure (the E step and the M

rting from parameter initialisation using random values. The

ties are estimated in the E step using the information of current

rameters, i.e., the cluster centres, the cluster covariance matrices

mixing coefficients. If model parameters have not yet been

d, information of probabilities are used to update model

rs in the M step. In other words, new model parameters are

d in the M step based on the current cluster membership function

hich are the probabilities.

mixture model algorithm has been widely employed for analysing

l/medical patterns. For instance, it has been used to improve the

of the infection prediction when investigating the Bovine viral

infection among cattle herds [Eze, et al., 2019]. The study

some interpretable epidemiologically clusters. Another recent

s employed the Gaussian mixture model for breast cancer

based on mRNA expression data which contains over 300

amples of breast cancer and healthy tissue [Prabakaran, et al.,

he study has demonstrated a significant value of a Gaussian

model for clinical practice. The Gamma mixture model has also

ployed in an image analysis for developing unbiased pre-

g system of biology images [Llera, et al., 2019] and in a HIV test

ysis project [Rice, et al., 2019].

e 2.35(a) shows a data which is a mixture of three Gaussian

Table 2.10 shows the cluster result of two algorithms on this data

otal accuracies were 88.5% and 90.5% for models constructed by

eans algorithm and the mixture model algorithm, respectively.

35(b) shows a data set which is a mixture of three Gamma

The clustering performance is shown in Table 2.11. The

s were 96.9% and 97.2% for the models constructed by the K-

gorithm and the mixture model algorithm, respectively.