文章基本信息

标题：FSSIKM: a Novel Approach for Brain Interaction Patterns
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作者：K.Vidhyadevia ; M.Beema Mehraj ; Dr.K.P.Kaliyamurthie 等
期刊名称：International Journal of Innovative Research in Science, Engineering and Technology
印刷版ISSN：2347-6710
电子版ISSN：2319-8753
出版年度：2015
卷号：4
期号：3
页码：1454
DOI：10.15680/IJIRSET.2015.0403091
出版社：S&S Publications
摘要：Functional magnetic resonance imaging (FMRI) patterns provides the prospective to study brainfunction in a non-invasive way. The FMRI data are time series of 3-dimensional volume images of the brain. The datais 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. Tounderstand the complex interaction patterns among brain regions our system proposes a novel clustering technique. Oursystem models each subject as multivariate time series, where the single dimensions represent the FMRI signal atdifferent anatomical regions. In our proposed system, there are three algorithms are used to mining the brain interactionpattern such as FSS, IKM and Dimension Ranking Algorithm. Feature subset selection (FSS) is a technique to preprocessthe data before performing any data mining tasks, e.g., classification and clustering. This technique was used tochoose a subset of the original features to be used for the subsequent processes. Hence, only the data generated fromthose features need to be collected. After that, select the key features in the preprocessed dataset based on the thresholdvalues. Interaction K-means (IKM), a partitioning clustering algorithm used to detect clusters of objects with similarinteraction patterns classification and clustering. Finally, Dimension Ranking algorithm was used to select the bestcluster for assuring best result.
关键词：Data mining; Clustering; Interaction K-means Algorithm; Feature Subset Selection; Dimension;Ranking; FMRI.