Abstract
The gene microarray data are arranged based on the pattern of gene expression using various clustering algorithms and the dynamic natures of biological processes are generally unnoticed by the traditional clustering algorithms. To overcome the problems in gene expression analysis, novel algorithms for finding the coregulated clusters, dimensionality reduction and clustering have been proposed. The coregulated clusters are determined using biclustering algorithm, so it is called as coregulated biclusters. The coregulated biclusters are two or more genes which contain similarity features. The dimensionality reduction of microarray gene expression data is carried out using Locality Sensitive Discriminant Analysis (LSDA). To maintain bond between the neighborhoods in locality, LSDA is used and an efficient meta heuristic optimization algorithm called Artificial Bee Colony (ABC) using Fuzzy C Means clustering is used for clustering the gene expression based on the pattern. The experimental results shows that proposed algorithm achieve a higher clustering accuracy and takes lesser less clustering time when compared with existing algorithms.
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Sathishkumar, K., Balamurugan, E., Narendran, P. (2013). An Efficient Artificial Bee Colony and Fuzzy C Means Based Co-regulated Biclustering from Gene Expression Data. In: Prasath, R., Kathirvalavakumar, T. (eds) Mining Intelligence and Knowledge Exploration. Lecture Notes in Computer Science(), vol 8284. Springer, Cham. https://doi.org/10.1007/978-3-319-03844-5_13
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DOI: https://doi.org/10.1007/978-3-319-03844-5_13
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