Effective K-means clustering algorithm for efficient data mining

Dubey, Paras; Rajavat, Anand

doi:10.1109/vitecon58111.2023.10157179

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Results and Discussion A Analysis Of Clustering Results1

Data Processing Methods1

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2023

2024

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Cited by 2 publications

(2 citation statements)

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“…The K-means clustering analysis does not directly incorporate geographical characteristics. However, geographical features incorporated into the data segregation can be recognized by the distance between the data points evaluated by the K-means clustering technique [60]. Furthermore, the DBSCAN clustering approach employs the parameters e and minPt [62].…”

Section: Results and Discussion A Analysis Of Clustering Resultsmentioning

confidence: 99%

Optimizing Urban-Scale Evacuation Strategies Through Disaster Victim Aggregation Modification

Asriandi Ekaputra,

Kee,

Yee

2024

IEEE Access

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Section: Results and Discussion A Analysis Of Clustering Resultsmentioning

confidence: 99%

Optimizing Urban-Scale Evacuation Strategies Through Disaster Victim Aggregation Modification

Asriandi Ekaputra,

Kee,

Yee

2024

IEEE Access

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“…5) Data Mining, data mining is the stage where data is processed using specific methods and algorithms to uncover valuable patterns and information. In this stage, the selection of data mining methods that are appropriate for the data and research objectives is carried out [24]. 6) Pattern Evaluation, the analysis, identification, and conclusion-making regarding the information and patterns discovered during data mining are conducted in the pattern evaluation stage.…”

Section: Data Processing Methodsmentioning

confidence: 99%

Application of Clustering-Based Data Mining for the Assessment of Nutritional Status in Toddlers at Community Health Centers

Fianty,

Johan,

Aulia

et al. 2023

J. Inf. Syst. Informatics

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Nutritional status is a crucial foundation for human health and development. Global facts indicate serious challenges in ensuring adequate nutrition, and the situation is no different in Indonesia. This research collected data from the Kelapa Dua Tangerang community health center and utilized data mining techniques with the k-means clustering algorithm to delve deeper into the nutritional status of toddlers. The research findings revealed that nearly 37.3% of toddlers experience issues with abnormal height or weight, as well as poor nutritional conditions, highlighting the importance of careful and timely intervention. With regular health monitoring by community health centers and active parental involvement, actions can be taken to support the optimal growth and development of these children. The results of this research provide a strong understanding to address malnutrition issues, which will ultimately support the formation of a healthier and more promising future generation in Indonesia.

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Effective K-means clustering algorithm for efficient data mining

Cited by 2 publications

References 9 publications

Optimizing Urban-Scale Evacuation Strategies Through Disaster Victim Aggregation Modification

Optimizing Urban-Scale Evacuation Strategies Through Disaster Victim Aggregation Modification

Application of Clustering-Based Data Mining for the Assessment of Nutritional Status in Toddlers at Community Health Centers

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