Cluster Implementation Based on the Belonging Function

Dividing data into groups and the ability to classify them by parameters allows you to process a large amount of information, as well as automate the process of analyzing this data. Automation of any process leads to an increase in the quality of the product, as well as a reduction in the implementation time. The paper considers clustering and its types as a possible application in the data processing process. Possible algorithms for finding the center of the cluster are analyzed, various behaviors of clusters are presented, which are put in analogy to membership functions.