Churn prediction analysis using various clustering algorithms in KNIME analytics platform

In data mining techniques, Clustering is a performed by grouping objects based on similarity of its characteristics to provide patterns and knowledge of given user data. Different types of clustering algorithms called partitioning, hierarchical and grid based clustering methods. Here k-means clustering, k-medoids clustering, Hierarchical clustering, DBSCAN and Fuzzy c means clustering. Clustering algorithms are used for customer churn analysis; one of the important reasons is that the cost of increasing a new customer is much higher than retaining an existing customer by using customer churn analysis. Initially KNIME analytics platform is used to analyse and visualization of data and later it is used to create model, rules and interactive views ofdata.