Min‐max kurtosis stratum mean: An improved K‐means cluster initialization approach for microarray gene clustering on multidimensional big data

Abstract

SUMMARY Microarray gene clustering is a big data application that employs the K‐means (KM) clustering algorithm to identify hidden patterns, evolutionary relationships, unknown functions and gene trends for disease diagnosis, tissue detection and biological analysis. The selection of initial centroids is a major issue in the KM algorithm because it influences the effectiveness, efficiency and local optima of the cluster. The existing initial centroid initialization algorithm is computationally expensive and degrades cluster quality due to the large dimensionality and interconnectedness of microarray gene data. To deal with this issue, this study proposed the min‐max kurtosis stratum mean (MKSM) algorithm for big data clustering in a single machine environment. The MKSM algorithm uses kurtosis for dimension selection, mean distance for gene relationship identification, and stratification for heterogeneous centroid extraction. The results of the presented algorithm are compared to the state‐of‐the‐art initialization strategy on twelve microarray gene datasets utilizing internal, external and statistical assessment criteria. The experimental results demonstrate that the MKSMKM algorithm reduces iterations, distance computation, data comparison and local optima, and improves cluster performance, effectiveness and efficiency with stable convergence.

Morphological Accuracy Data Clustering: A Novel Algorithm for Enhanced Cluster Analysis

“…By examining diferent methods for selecting initial centroids, we can evaluate their impact on the clustering results and overall algorithm performance. Tis analysis will be conducted using simple simulated student datasets, which will allow us to assess the algorithm's effectiveness in real-world scenarios [20][21][22][23][24].…”

“…(i) For the chosen center elements C 1 � β1, β4, β5, 􏼈 β6, β7} and C 2 � β2, β3, β8 􏼈 􏼉. Applying the formulas ( 22) and (23) gives the values B � 21070 and W � 25632. Hence, the Calinski-Harabasz index is C � 4.9.…”