fuzzy clustering

Various chemical compounds are used in industry. Many industries maintain the results of chemical compounds. In this case, maintaining and using existing chemical data poses a challenge. If the amount of this chemical data increases, a model for data clustering is needed to be able to separate the data of different compounds. Clustering Finds data with similar properties in separate clusters without having prior knowledge of the available data. In the chemical industry, it is not possible to label all chemical composition data because they may occur or change at any time. In this case, clustering should be used, which divides the chemical data into a number of subsets. From a data mining point of view, chemical data detection is one of the issues of data marginalization. By introducing appropriate algorithms in this field and then trying to increase the efficiency and accuracy of chemical information, we can take steps to create highly reliable mechanized systems with the ability to detect complex patterns. Here, a series of data on the chemical composition of different industries are collected and clustering is performed with the help of a suitable hybrid model. The proposed method is a hybrid model of the nearest neighborhood using fuzzy clustering. In this model, the existing chemical data is subjected to a preprocessing operation to remove inappropriate and empty data from the system. The clustering operation is then performed with the nearest neighbor model.

Various chemical compounds are used in industry. Many industries maintain the results of chemical compounds. In this case, maintaining and using existing chemical data poses a challenge. If the amount of this chemical data increases, a model for data clustering is needed to be able to separate the data of different compounds. Clustering Finds data with similar properties in separate clusters without having prior knowledge of the available data. In the chemical industry, it is not possible to label all chemical composition data because they may occur or change at any time. In this case, clustering should be used, which divides the chemical data into a number of subsets. From a data mining point of view, chemical data detection is one of the issues of data marginalization. By introducing appropriate algorithms in this field and then trying to increase the efficiency and accuracy of chemical information, we can take steps to create highly reliable mechanized systems with the ability to detect complex patterns. Here, a series of data on the chemical composition of different industries are collected and clustering is performed with the help of a suitable hybrid model. The proposed method is a hybrid model of the nearest neighborhood using fuzzy clustering. In this model, the existing chemical data is subjected to a preprocessing operation to remove inappropriate and empty data from the system. The clustering operation is then performed with the nearest neighbor model.