Inter Cluster Migration Estimation (ICME) model based on Cluster parameters

Clustering is one of the methods that can be used on large datasets for knowledge discovery. Several attempts have been made in the past to study the effects of the incoming new objects into the existing clusters. In these lines, this work attempts to build an Inter Cluster Migration Estimation (ICME) model to estimate a value for the new entrant that will effect a migration of points between clusters. A better prediction will result in the reduction of number of times re-clustering is done on the dataset. Synthetic data sets of higher dimensions are generated and the estimated model is found to be in concurrence with the experimental setup with a lower error rate.