Initial Centroids for K-Means Using Nearest Neighbors and Feature Means

Muddana, Akkalakshmi; Daniel, Gera Victor; Rao, D. Srinivasa

doi:10.1007/978-981-13-3600-3_3

Cited by 10 publications

(6 citation statements)

References 8 publications

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“…But, execution time was trivial for the dataset experimented by authors. M. A. Lakshmi et al [13] have proposed a method to find initial centroids by using the nearest neighbor method. They compared their idea by using SSE(Sum of the Squared Differences) with random and kmeans++ initial selection.…”

Section: Related Workmentioning

confidence: 99%

An Efficient K-Means Clustering Algorithm for Analysing COVID-19

Zubair

Iqbal

Shil

et al. 2021

Hybrid Intelligent Systems

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COVID-19 hits the world like a storm by arising pandemic situations for most of the countries around the world. The whole world is trying to overcome this pandemic situation. A better health care quality may help a country to tackle the pandemic. Making clusters of countries with similar types of health care quality provides an insight into the quality of health care in different countries. In the area of machine learning and data science, the K-means clustering algorithm is typically used to create clusters based on similarity. In this paper, we propose an efficient K-means clustering method that determines the initial centroids of the clusters efficiently. Based on this proposed method, we have determined health care quality clusters of countries utilizing the COVID-19 datasets. Experimental results show that our proposed method reduces the number of iterations and execution time to analyze COVID-19 while comparing with the traditional k-means clustering algorithm.

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Section: Related Workmentioning

confidence: 99%

An Efficient K-Means Clustering Algorithm for Analysing COVID-19

Zubair

Iqbal

Shil

et al. 2021

Hybrid Intelligent Systems

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“…The centroid method maximized the joint probability of pixel position and detected threshold distance for cluster data arrangements. Lakshmi et al 61 developed a KM initialization algorithm using feature‐mean and nearest neighbors (FMNN) approach. The FMNN algorithm initially computes the mean of each dimension and then discards the N/K nearest neighbor data points for initial centroid detection.…”

Section: Related Workmentioning

confidence: 99%

“…This study compared the proposed algorithm to the standard random KM, 22 KM++, 40,41 ADV, 66,98 MKM, 49 Mean‐KM, 51 NFD, 54 K‐MAM, 56 NRKM2, 58 FMNN, 61 and MuKM 63 algorithms. These algorithms achieve better efficiency, effectiveness, stable results and convergence with nondeterministic and nondensity behavior.…”

Section: Experimental Analysismentioning

confidence: 99%

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

Pandey

Shukla

2022

Concurrency and Computation

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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.

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“…Many literature have solved the initialization problem of k-means clustering algorithm [11,14,25,27,33,42]. For example, Duan et al developed calculating the density to select the initial centroids [14].…”

Section: Introductionmentioning

confidence: 99%

“…For example, Duan et al developed calculating the density to select the initial centroids [14]. Lakshmi et al proposed to use nearest neighbors and feature means to decide the initial centroids [25]. Meanwhile, many researches addressed the similarity problem of k-means clustering algorithm [4,34,37,39,40,54].…”

Section: Introductionmentioning

confidence: 99%

Initialization-similarity clustering algorithm

Liu

Zhu

Zhou

et al. 2019

Multimed Tools Appl

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Classic k-means clustering algorithm randomly selects centroids for initialization to possibly output unstable clustering results. Moreover, random initialization makes the clustering result hard to reproduce. Spectral clustering algorithm is a two-step strategy, which first generates a similarity matrix and then conducts eigenvalue decomposition on the Laplacian matrix of the similarity matrix to obtain the spectral representation. However, the goal of the first step in the spectral clustering algorithm does not guarantee the best clustering result. To address the above issues, this paper proposes an Initialization-Similarity (IS) algorithm which learns the similarity matrix and the new representation in a unified way and fixes initialization using the sum-ofnorms regularization to make the clustering more robust. The experimental results on ten realworld benchmark datasets demonstrate that our IS clustering algorithm outperforms the comparison clustering algorithms in terms of three evaluation metrics for clustering algorithm including accuracy (ACC), normalized mutual information (NMI), and Purity.

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Initial Centroids for K-Means Using Nearest Neighbors and Feature Means

Cited by 10 publications

References 8 publications

An Efficient K-Means Clustering Algorithm for Analysing COVID-19

An Efficient K-Means Clustering Algorithm for Analysing COVID-19

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

Initialization-similarity clustering algorithm

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