FSSIKM: a Novel Approach for Brain Interaction Patterns

摘要

Functional magnetic resonance imaging (FMRI) patterns provides the prospective to study brain function in a non-invasive way. The FMRI data are time series of 3-dimensional volume images of the brain. The data is traditionally analyzed within a mass-univariate framework essentially relying on classical inferential statistics. Handling of feature selection and clustering is a complicated process in Interaction patterns of brain datasets. To understand the complex interaction patterns among brain regions our system proposes a novel clustering technique. Our system models each subject as multivariate time series, where the single dimensions represent the FMRI signal at different anatomical regions. In our proposed system, there are three algorithms are used to mining the brain interaction pattern such as FSS, IKM and Dimension Ranking Algorithm. Feature subset selection (FSS) is a technique to preprocess the data before performing any data mining tasks, e.g., classification and clustering. This technique was used to choose a subset of the original features to be used for the subsequent processes. Hence, only the data generated from those features need to be collected. After that, select the key features in the preprocessed dataset based on the threshold values. Interaction K-means (IKM), a partitioning clustering algorithm used to detect clusters of objects with similar interaction patterns classification and clustering. Finally, Dimension Ranking algorithm was used to select the best cluster for assuring best result.