# A Taxonomy of Machine Learning Clustering Algorithms, Challenges, and Future Realms
## 2 Taxonomy of Clustering Algorithms
### Partitional Clustering Algorithm
#### 2.2.3 Mixture Resolving Algorithms  
The assumption made by the mixture-based method, also known as the mixture resolving algorithm, is that a group of observed objects originates from a combination of examples from several probability clusters. In order to produce each observed item, a probabilistic cluster is picked in accordance with the cluster's probability. After that, a sample is selected based on the selected cluster's probability density function. The data set is taken to be a combination of a certain number of distinct cluster groups that were clustered in varied proportions. The mixture likelihood-based method of clustering is model-based since it requires the pre-specification of each component's observational component density. To cluster samples from a population [82] noted that the statistical model to be employed must be specified or understood beforehand. Due to the similarity among model-based and mixture-based, simple regression concepts may be used to do prediction evaluation and hypotheses development. The mixture likelihood-based strategy, according to [82] is \"probably the only clustering technique that is totally adequate from the mathematical point of view.\" It assumes a well-specified mathematical model, exploresit using well-known statistical techniques, and offers a test of the result's significance. A mixture-based method can readily determine the optimal number of clusters since it has a solid probabilistic foundation. Thus, according to [83], several of the advantages of the mixture model is that it successfully combines different data sets using different scientific methods. However, the processing complexity is significant, and the assumptions made about the distribution of the data are rather strong. Additionally, each cluster is seen as a single simple distribution, which limits the formation of the cluster [84].
* Expectation maximization In data-driven methods developed, the EM technique for estimation methods has been frequently employed. EM is a statistical inference technique that guarantees the conditional probability will converge [85]. According to the parameters of the probabilistic clusters, objects are allocated to clusters in the expectation stage, and in the maximization stage, a new grouping or feature is found that improves the projected probability. Given the initial values drawn at random for the parameters of the probabilistic distribution, such as through and until the metric aligns or the shift is negligible, the average, the standard deviation, the E-step, and the M-step are repeated at regular intervals. The chance that each item belongs to each distribution is determined during clustering, and the probabilistic distribution parameter is changed to maximize the predicted likelihood of each cluster object in the M-step. The EM method requires several calculations for each iteration. As a result of this iterative calculation, the amount of data points and combination components scaled linearly, limiting the EM algorithm's applicability for large-scale applications [67; 86]. The EM technique is straightforward since it does not need the setting of any factors that might influence the optimization process [67].