A Frequent Itemset-Nearest Neighbor Based Approach for Clustering Gene
Expression Data
Rosy Das, D.K. Bhattacharyya and
J.K. Kalita
Microarray technology has enabled the monitoring of
expression levels of thousands of genes across different experimental
conditions. Identifying groups of genes that manifest
similar expression patterns in such huge amounts of data is
crucial in the analysis of gene expression time series. In this
study, we present an integrated analysis of microarray data
using association mining and clustering that discovers intrinsic
grouping based on co-occurrence patterns in such data. A shared
nearest neighbor approach is used to cluster the results of
association mining to obtain the final clustering of the dataset.
The method was used with real-life datasets and has been found
to perform satisfactorily.
Index Terms
Gene expression, microarray, coherent pattern, association mining, clustering.
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