Evaluation of an Optimized K-Means Algorithm Based on Real Data

Poteras, Cosmin Marian; Mocanu, Mihai

doi:10.15439/2016f231

Cited by 3 publications

(3 citation statements)

References 12 publications

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“…Set C comprises objects that have no missing data, while set M contains those that have missing values. Objects in set C are clustered using conventional algorithms, such as K-means [30], under the assumption that since these objects have no missing values, the level of uncertainty is low, and conventional approaches are more suitable for clustering such objects (Figure 1 (1)).…”

Section: A Three-way Clusteringmentioning

confidence: 99%

Clustering Corticosteroids Responsiveness in Sepsis Patients using Game-Theoretic Rough Sets

Hellali,

Dagdia,

Zeitouni

2023

Annals of Computer Science and Information Systems

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Performing data mining tasks in the medical domain poses a significant challenge, mainly due to the uncertainty present in patients' data, such as incompleteness or missingness. In this paper, we focus on the data mining task of clustering corticosteroid (CS) responsiveness in sepsis patients. We address the issue and challenge of missing data by applying Game-Theoretic Rough Sets (GTRS) as a three-way decision approach. Our study considers the APROCCHS cohort, comprising 1240 sepsis patients, provided by the Assistance Publique-Hôpitaux de Paris (AP-HP), France. Our experimental results on the APROCCHS cohort indicate that GTRS maintains the trade-off between accuracy and generality, demonstrating its effectiveness even when increasing the number of missing values.

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Section: A Three-way Clusteringmentioning

confidence: 99%

Clustering Corticosteroids Responsiveness in Sepsis Patients using Game-Theoretic Rough Sets

Hellali,

Dagdia,

Zeitouni

2023

Annals of Computer Science and Information Systems

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“…To use K, silhouette measures are used. The cluster can be ranked or classified to get an enhanced student performance [5,6]. This algorithm may help to get a student performance result in a ranking manner [7].…”

Section: Literature Surveymentioning

confidence: 99%

Employee Performance Appraisal System Based on Ranking and Reviews

Modi

Muralidhar

2017

Asian J Pharm Clin Res

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Objective: In many organizations, employee data have to be maintained and utilized for many purposes. Here, in this paper, we are going to use such data to calculate an employee's performance.Methods: This employee data may be converted into useful information using data mining techniques such as K-means and decisions tree. K-means is used to find the rank of the employee means that the employee may come under in his criteria. Decision tree is used to find the review of an employee means that the employee needs improvement or he/she meets expectation.Results: This algorithm when utilized can identify the top employee who can be considered for appraisal or the eligible candidates for promotion. Hence, these algorithms such as K-mean and decision tree that help to find best employees for any association and help us to take a good decision in less time. Conclusion:There are various factors which should be considered and are limited to this algorithm, so human intervention is required to consider those factors. However, ranking and appraisal are seen in many companies, and this algorithm will definitely identify the potential candidates.

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“…C LUSTERING is an unsupervised learning technique that seeks to divide a collection of data objects into a set of related classes [1], [2], [3]. It is a crucial and challenging subject in data mining and machine learning, and it has been successfully applied in a wide range of fields, including image processing [4], recommender systems [5], text mining [6], and pattern recognition [7].…”

Section: Introductionmentioning

confidence: 99%