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Performance analysis of clustering techniques over microarray data: A case study

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  • Dash, Rasmita
  • Misra, Bijan Bihari

Abstract

Handling big data is one of the major issues in the field of statistical data analysis. In such investigation cluster analysis plays a vital role to deal with the large scale data. There are many clustering techniques with different cluster analysis approach. But which approach suits a particular dataset is difficult to predict. To deal with this problem a grading approach is introduced over many clustering techniques to identify a stable technique. But the grading approach depends on the characteristic of dataset as well as on the validity indices. So a two stage grading approach is implemented. In this study the grading approach is implemented over five clustering techniques like hybrid swarm based clustering (HSC), k-means, partitioning around medoids (PAM), vector quantization (VQ) and agglomerative nesting (AGNES). The experimentation is conducted over five microarray datasets with seven validity indices. The finding of grading approach that a cluster technique is significant is also established by Nemenyi post-hoc hypothetical test.

Suggested Citation

  • Dash, Rasmita & Misra, Bijan Bihari, 2018. "Performance analysis of clustering techniques over microarray data: A case study," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 493(C), pages 162-176.
    • Handle: RePEc:eee:phsmap:v:493:y:2018:i:c:p:162-176
    DOI: 10.1016/j.physa.2017.10.032
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    References listed on IDEAS

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    1. Gribkova, Svetlana, 2015. "Vector quantization and clustering in the presence of censoring," Journal of Multivariate Analysis, Elsevier, vol. 140(C), pages 220-233.
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    Cited by:

    1. Chen, Shunjie & Yang, Sijia & Wang, Pei & Xue, Liugen, 2023. "Two-stage penalized algorithms via integrating prior information improve gene selection from omics data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 628(C).

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