Mitigating the Risk of Customer Churn Using K-Means Clustering

The probability that a customer is not benefited by investing in a particular share, while there is still a chance for calculating accurate result that changes in milliseconds, has a huge impact on the profit for the customer as well as the organization they is associated with. Considering this criteria, a new clustering algorithm called the K-Means clustering method (KMC) is proposed. There are speculative results that witness K-means has stronger clustering semantic strength than other clustering methods in data mining. Can also get suggestions to avoid the risk of investing in an unprofitable share.