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Min max kurtosis distance based improved initial centroid selection approach of K-means clustering for big data mining on gene expression data

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EVOLVING SYSTEMS 2023年第2期14卷 207-244页

作者： Pandey, Kamlesh Kumar Shukla, Diwakar Dr Hari Singh Gour Vishwavidyalaya Dept Comp Sci & Applicat Sagar MP India Dr Hari Singh Gour Vishwavidyalaya Dept Math & Stat Sagar India

Genome clustering is one of the big data applications that identify the prognosis of terrifying diseases and biological processes across enormous sets of genes. The K-Means (KM) algorithm is the most commonly used clustering algorithm for gene expression data that extracts hidden knowledge, patterns and trends from gene expression profiles for decision-making strategies. Unfortunately, the KM algorithm is extremely sensitive to initial centroid selection since the initial centroid of clusters influences computational effectiveness, efficiency, cost and local optima issues. The existing initial centroid initialization algorithm attains high computational complexity due to extensive iterations, distance computation, data and result comparison on high dimensional data. To overcome these weaknesses, this study suggested the Min-Max Kurtosis Distance (MMKD) algorithm for big data clustering in a single machine environment. The MMKD algorithm resolves the KM clustering weaknesses by the distance between data points of origin and minimum-maximum kurtosis dimension. The performance of the proposed algorithm is compared to KM, KM++ , ADV, MKM, Mean-KM, NFD, K-MAM, NRKM2, FMNN and MuKM algorithms by internal and external effectiveness validation metrics with efficiency measurement on sixteen gene expression datasets. The experimental evaluation demonstrates that the MMKDKM algorithm reduces iterations, local optima, computation costs, and improves cluster performance, effectiveness and efficiency with stable convergence than other algorithms. The statistical analysis of this study promised that the proposed MMKDKM algorithm achieves a significant difference.

关键词： Big data clustering initial centroid algorithm Genome clustering Gene expression data clustering Kurtosis clustering Systematic sampling Sorting heuristic Convergence speed K-means

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Min-max kurtosis mean distance based k-means initial centroid initialization method for big genomic data clustering

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EVOLUTIONARY INTELLIGENCE 2023年第3期16卷 1055-1076页

作者： Pandey, Kamlesh Kumar Shukla, Diwakar Dr Hari Singh Gour Vishwavidyalaya Dept Comp Sci & Applicat Sagar Madhya Pradesh India

Genomic clustering is a big data application that uses the K-means (KM) clustering approach to discover hidden patterns and trends in genes for disease diagnosis, biological analysis, and tissue detection. The KM algorithm is highly dependent on the initial centroid because it determines the effectiveness, efficiency, computing resources, and local optima of the KM clustering. The existing initial centroid initialization approach traps local optima due to randomization and achieves high computational cost due to the enormous interrelated dimension. Therefore, the KM algorithm produces the lowest quality cluster and maximizes the computation time and resource consumption. To address this issue, this study has presented the Min-Max Kurtosis Mean Distance (MKMD) algorithm for big data clustering in a single machine environment. The MKMD algorithm enhances the effectiveness and efficiency of the KM algorithm by measuring the distance between data points of the minimum-maximum kurtosis dimension and their mean. The performance of the presented algorithm has been compared against KM, KM + + , ADV, MKM, Mean-KM, NFD, K-MAM, NRKM2, FMNN and MuKM algorithms using internal and external effectiveness evaluation criteria with efficiency assessment on sixteen genomic datasets. The experimental results reveal that the MKMDKM algorithm minimizes iterations, distance computation, data comparison, local optima, resource consumption, and improves cluster performance, effectiveness and efficiency with stable convergence and results as compared to other algorithms. According to the statistical analysis, the proposed MKMDKM algorithm has achieved statistical significance by employing the Friedman test and the post hoc test.

关键词： Big data clustering initial centroid algorithm Genome clustering Kurtosis clustering Convergence speed K-Means

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NDPD: an improved initial centroid method of partitional clustering for big data mining

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JOURNAL OF ADVANCES IN MANAGEMENT RESEARCH 2023年第1期20卷 1-34页

作者： Pandey, Kamlesh Kumar Shukla, Diwakar Dr Hari Singh Gour Vishwavidyalaya Sagar India

Purpose The K-means (KM) clustering algorithm is extremely responsive to the selection of initial centroids since the initial centroid of clusters determines computational effectiveness, efficiency and local optima issues. Numerous initialization strategies are to overcome these problems through the random and deterministic selection of initial centroids. The random initialization strategy suffers from local optimization issues with the worst clustering performance, while the deterministic initialization strategy achieves high computational cost. Big data clustering aims to reduce computation costs and improve cluster efficiency. The objective of this study is to achieve a better initial centroid for big data clustering on business management data without using random and deterministic initialization that avoids local optima and improves clustering efficiency with effectiveness in terms of cluster quality, computation cost, data comparisons and iterations on a single machine. Design/methodology/approach This study presents the Normal Distribution Probability Density (NDPD) algorithm for big data clustering on a single machine to solve business management-related clustering issues. The NDPDKM algorithm resolves the KM clustering problem by probability density of each data point. The NDPDKM algorithm first identifies the most probable density data points by using the mean and standard deviation of the datasets through normal probability density. Thereafter, the NDPDKM determines K initial centroid by using sorting and linear systematic sampling heuristics. Findings The performance of the proposed algorithm is compared with KM, KM++, Var-Part, Murat-KM, Mean-KM and Sort-KM algorithms through Davies Bouldin score, Silhouette coefficient, SD Validity, S_Dbw Validity, Number of Iterations and CPU time validation indices on eight real business datasets. The experimental evaluation demonstrates that the NDPDKM algorithm reduces iterations, local optima, computing costs, and

关键词： Big data clustering initial centroid algorithm Business data clustering Normal distribuation Probability density Systematic sampling Sorting heuristic Convergence speed K-means K-means plus plus

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Maxmin distance sort heuristic-based initial centroid method of partitional clustering for big data mining

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PATTERN ANALYSIS AND APPLICATIONS 2022年第1期25卷 139-156页

作者： Pandey, Kamlesh Kumar Shukla, Diwakar Dr Hari Singh Gour Vishwavidyalaya Dept Comp Sci & Applicat Sagar Madhya Pradesh India

The revolution of digital and communication technologies is producing an enormous amount of data. Therefore, the nature of classical data changes into big data, and mining techniques have to face high computation cost, performance and scalability-related challenges. The K-means (KM) algorithm is the most widely used partitional clustering approach that depends on K clusters, initial centroid, distance measures and central tendency statistical approaches. The initial centroid determines the computational effectiveness, efficiency and local optima issues in big data clustering due to the gradient descent nature of the KM algorithm. The existing centroid initialization algorithm has achieved low cluster quality with high computational complexity due to iterations, distance computation, data and result comparison. To overcome these deficiencies, this paper presents the Maxmin Distance Sort Heuristic (MDSH) algorithm for big data clustering through a stratified sampling process. The performance of the MDSHKM algorithm is compared with the KM and KM++ algorithms through R square, Root-Mean-Square Standard Deviation, Davies-Bouldin score, Calinski Harabasz score, Silhouette Coefficient, Number of Iterations and CPU time validation indices using eight real datasets. The experimental evaluation shows that the MDSHKM algorithm achieves better cluster quality, computing cost, efficiency and stable convergence than the KM and KM++ algorithms.

关键词： Big data clustering initial centroid algorithm Convergence speed Stratified sampling K-means K-means plus plus MDSHK-means

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