![]() ![]() The clustering technique consists of two parts: (1) a sequential statistical clustering which is essentially a sequential variance analysis, and (2) a generalized K-means clustering. ![]() The composite sequential clustering technique for analysis of multispectral scanner data ![]() The finding of grading approach that a cluster technique is significant is also established by Nemenyi post-hoc hypothetical test. The experimentation is conducted over five microarray datasets with seven validity indices. 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). So a two stage grading approach is implemented. But the grading approach depends on the characteristic of dataset as well as on the validity indices. To deal with this problem a grading approach is introduced over many clustering techniques to identify a stable technique. But which approach suits a particular dataset is difficult to predict. There are many clustering techniques with different cluster analysis approach. In such investigation cluster analysis plays a vital role to deal with the large scale data. Handling big data is one of the major issues in the field of statistical data analysis. Performance analysis of clustering techniques over microarray data: A case study ![]()
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