Comparative Analysis and Evaluation of Biclustering Algorithms for Microarray Data

  • Ankush MaindEmail author
  • Shital Raut
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 4)


From the last decade, the concept of biclustering becomes very popular for the analysis of gene expression data. This is because of the advantages of biclustering algorithms over the drawbacks of clustering algorithms on gene expression data. Many biclustering algorithms have been published in recent years. Some of them performed well on gene expression data and other have some issues. In this paper, analysis of some popular biclustering algorithms have been done with the help of experimental study. Along with this, survey of all the bicluster quality measures which have been used for extracting biologically significant biclusters in various biclustering algorithms is also given. For the experimental study, synthetic dataset has been used. Based on the experimental study, some comparative analyses have been done, and some important issues related to the biclustering algorithms have been pointed out. From this analytical as well as experimental study, newcomers who are interested to do the research in the area of biclustering will get proper direction for the better research.


Biclustering Gene expression data Biologically significant etc. 


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© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Computer Science & Engineering DepartmentVNITNagpurIndia

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