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

Rasmita Dash and Bijan Bihari Misra

Physica A: Statistical Mechanics and its Applications, 2018, vol. 493, issue C, 162-176

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.

Keywords: Microarray data; Feature selection; Cluster analysis; Particle swarm optimization; Statistical test (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:493:y:2018:i:c:p:162-176

DOI: 10.1016/j.physa.2017.10.032

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