Comparison Of Data Collection Algorithms Using Some Mixed Distributions

The current research aims to compare different data clustering algorithms, focusing on algorithms that use Mixture distributions. We will discuss how these algorithms work, their advantages and disadvantages, and their efficiency in clustering diverse data in size and structure. A comprehensive analysis will also be conducted by applying these algorithms to multiple data sets to evaluate the performance, efficiency, and accuracy of clustering by using them in Mixture distributions. (Mixture Exponential Distribution, Mixture Weibull Distribution, Mixture Pareto distribution) were chosen as applications to study clustering algorithms. Comparing different data clustering algorithms when using Mixture distributions, which are a type of statistical models that depend on merging several probability distributions to represent data, as well as the widespread use of these algorithms in data analysis and extracting patterns of that data, which makes them a powerful tool in many practical applications such as classification, pattern recognition, and statistical predictions. After a detailed presentation of the different clustering algorithms, the algorithm evaluation mechanism, and some Mixture distributions, the researcher concluded that each algorithm has a work that cannot be dispensed with or replaced and that all algorithms are highly efficient in their field of work if the conditions and specifications of each algorithm are adhered to. Therefore, the researcher recommended dealing with these algorithms, each according to its work, to obtain the best results.