DK-means: a deterministic K-means clustering algorithm for gene expression analysis

Jothi, R.; Mohanty, Sraban Kumar; Ojha, Aparajita

doi:10.1007/s10044-017-0673-0

Cited by 50 publications

(31 citation statements)

References 39 publications

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“…Cluster analysis is, when the category of studied object is not known in advance, to group the similarities into one category based on the degree of affinity, so that the same category can achieve the maximum homogeneity and minimize the heterogeneity, while the different categories achieve the maximum homogeneity and minimum heterogeneity [5]. Clustering analysis algorithms can be summarized into three different types: trying to find an optimal partition to divide the data into a specified number of clusters; trying to find a method of clustering structure hierarchy; and trying to find a method based on probability model for potential cluster modeling [6]. e K-means cluster analysis method can effectively avoid the subjective negative impact caused by the artificial threshold value, so it can more accurately and objectively distinguish the state intervals of different financial risks.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Early Warning of Financial Risk Based on K‐Means Clustering Algorithm

Zhu

Liu

2021

Complexity

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The early warning of financial risk is to identify and analyze existing financial risk factors, determine the possibility and severity of occurring risks, and provide scientific basis for risk prevention and management. The fragility of financial system and the destructiveness of financial crisis make it extremely important to build a good financial risk early-warning mechanism. The main idea of the K-means clustering algorithm is to gradually optimize clustering results and constantly redistribute target dataset to each clustering center to obtain optimal solution; its biggest advantage lies in its simplicity, speed, and objectivity, being widely used in many research fields such as data processing, image recognition, market analysis, and risk evaluation. On the basis of summarizing and analyzing previous research works, this paper expounded the current research status and significance of financial risk early-warning, elaborated the development background, current status and future challenges of the K-means clustering algorithm, introduced the related works of similarity measure and item clustering, proposed a financial risk indicator system based on the K-means clustering algorithm, performed indicator selection and data processing, constructed a financial risk early-warning model based on the K-means clustering algorithm, conducted the classification of financial risk types and optimization of financial risk control, and finally carried out an empirical experiments and its result analysis. The study results show that the K-means clustering method can effectively avoid the subjective negative impact caused by artificial division thresholds, continuously optimize the prediction process of financial risk and redistribute target dataset to each cluster center for obtaining optimized solution, so the algorithm can more accurately and objectively distinguish the state interval of different financial risks, determine risk occurrence possibility and its severity, and provide a scientific basis for risk prevention and management. The study results of this paper provide a reference for further researches on financial risk early-warning based on K-means clustering algorithm.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Early Warning of Financial Risk Based on K‐Means Clustering Algorithm

Zhu

Liu

2021

Complexity

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“…Later, the k-means|| algorithm was proposed by Bahmani et al and was shown to perform better than k-means++ in both sequential and parallel settings [8]. In the heuristic algorithm's side, a deterministic initialization algorithm for k-means (Dk-means) was proposed by Jothi et al, which explored a set of probable centers based on a constrained bi-partitioning approach, and achieved a remarkable convergence speed and stability [10].…”

Section: Related Workmentioning

confidence: 99%

Improved Constrained k-Means Algorithm for Clustering with Domain Knowledge

et al. 2021

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Witnessing the tremendous development of machine learning technology, emerging machine learning applications impose challenges of using domain knowledge to improve the accuracy of clustering provided that clustering suffers a compromising accuracy rate despite its advantage of fast procession. In this paper, we model domain knowledge (i.e., background knowledge or side information), respecting some applications as must-link and cannot-link sets, for the sake of collaborating with k-means for better accuracy. We first propose an algorithm for constrained k-means, considering only must-links. The key idea is to consider a set of data points constrained by the must-links as a single data point with a weight equal to the weight sum of the constrained points. Then, for clustering the data points set with cannot-link, we employ minimum-weight matching to assign the data points to the existing clusters. At last, we carried out a numerical simulation to evaluate the proposed algorithms against the UCI datasets, demonstrating that our method outperforms the previous algorithms for constrained k-means as well as the traditional k-means regarding the clustering accuracy rate although with a slightly compromised practical runtime.

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“…This hybrid algorithm was used to solve the problem of the counterfort retaining walls. The k-means technique has been widely used and in recent studies, it has been applied in [59] to bioinformatics for detecting gene expression profile, image segmentation for pest detection [60] in agriculture, and brain tumor identification [61], among others. Particularly the k-means technique has been previously applied in obtaining binary versions of continuous metaheuristics and used to solve the multidimensional knapsack problem [33] and the set covering problem [5] which are NP-hard problems.…”

Section: Hybridizing Metaheuristics With Machine Learningmentioning

confidence: 99%

A Hybrid k-Means Cuckoo Search Algorithm Applied to the Counterfort Retaining Walls Problem

2020

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The counterfort retaining wall is one of the most frequent structures used in civil engineering. In this structure, optimization of cost and CO2 emissions are important. The first is relevant in the competitiveness and efficiency of the company, the second in environmental impact. From the point of view of computational complexity, the problem is challenging due to the large number of possible combinations in the solution space. In this article, a k-means cuckoo search hybrid algorithm is proposed where the cuckoo search metaheuristic is used as an optimization mechanism in continuous spaces and the unsupervised k-means learning technique to discretize the solutions. A random operator is designed to determine the contribution of the k-means operator in the optimization process. The best values, the averages, and the interquartile ranges of the obtained distributions are compared. The hybrid algorithm was later compared to a version of harmony search that also solved the problem. The results show that the k-mean operator contributes significantly to the quality of the solutions and that our algorithm is highly competitive, surpassing the results obtained by harmony search.

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DK-means: a deterministic K-means clustering algorithm for gene expression analysis

Cited by 50 publications

References 39 publications

[Retracted] Early Warning of Financial Risk Based on K‐Means Clustering Algorithm

[Retracted] Early Warning of Financial Risk Based on K‐Means Clustering Algorithm

Improved Constrained k-Means Algorithm for Clustering with Domain Knowledge

A Hybrid k-Means Cuckoo Search Algorithm Applied to the Counterfort Retaining Walls Problem

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