Model based essential interactions cluster mining in multivariate time

Functional magnetic resonance imaging or functional MRI (fMRI) is a functional neuroimaging procedure using MRI technology that measures brain activity by detecting associated changes in blood flow. The goal of fMRI data analysis is to detect correlations between brain activation and a task the subject performs during the scan. It also aims to discover correlations with the specific cognitive states, such as memory and recognition, induced in the subject. In this system, we propose a novel framework for clustering the essential fMRI signals based on their interactions and also correlation which is generated in a multivariate time series. To formalize this framework we cluster only Important Interactions based on the patient´s medical records with the help of Essential Clustering Algorithm. The Essential clusters (EC) are then clustered again based on their dependencies on various brain regions. These EC´s are grouped under specific models. The changes detected are mined based on the type of cluster grouped under a certain model. Our method shows that certainly increases the efficiency of the system along with increases in the effectiveness with minimal resource utilization.