Classifying Data Using Artificial Intelligence K-Means Clustering Algorithm

"The primary aim of clustering is not just to make clusters, but to make good and meaningful ones" – Analytics Vidhya (https://www.analyticsvidhya.com/). An optimal multi-objective clustering is one of the most popular, and, at the same time, curious supervised machine learning problems, that occurs in many fields of computer science such as data and knowledge mining, data compression, vector quantization, patterns detection and classification, Voronoi diagrams, recommender engines (RE), etc. The process of clustering analysis itself allows us to reveal various of trends and insights exhibited on the input dataset. The cluster analysis (CA) process allows us to determine the similarities and differences between specific data, partitioning the data in such as a way that the similar data normally belongs to a specific group or cluster. For example, we can perform the clustering analysis of the data on a credit card customer to reveal what special offers should be given to a specific customer, based on the balance and loan amount criteria. In this case, all that we have to do is to partition all customers data into the number of clusters, and, then give the same offer to the similar customers. This is typically done by performing the multi-variate numerical data the multi-variate numerical data clustering analysis. The main goal of performing the actual clustering is to arrange a set of data items having an associated numeric n-dimensional vector of features into the number of homogeneous groups, called - "clusters".