Comparison of Clustering K-Means,  Fuzzy C-Means, and Linkage for Nasa Active Fire Dataset

Kurniawan, Muchamad; Muhima, Rani Rotul; ., Dewa Ayu Eka Agustini, S.Pd., M.S.

doi:10.25139/ijair.v2i2.3030

Cited by 6 publications

(3 citation statements)

References 11 publications

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“…Subsequently, the curve provided information about the number of clusters used. For example, when the value of the rst and second gave the angle on the graph or the value that had decreased the most, the number can be used (Kurniawan et al, 2020) . 3.…”

Section: Methodsmentioning

confidence: 99%

The Risk Cluster in Type 2 Diabetes Mellitus Based on Risk Parameters Using Fuzzy C-Means Algorithm

2023

Sci. Technol. Indones

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The prevalence of type 2 diabetes mellitus increases every year. In the long term, type 2 diabetes mellitus can lead to complications of other diseases. This study aimed to analyze the risk cluster for type 2 diabetes mellitus based on risk parameters using the Fuzzy C-Means algorithm. The benefit of analyzing the risk cluster as an initial screening to prevent the occurrence of type 2 diabetes mellitus. This study used 905 subjects’ data consisting of 562 males and 343 females. After the data preprocessing, the optimal number of clusters was determined using a Fuzzy C-Means algorithm process. Subsequently, the Pearson correlation test was conducted to determine the correlation between the risk parameters of type 2 diabetes mellitus and the cluster results. The study resulted in 2 risk clusters, subjects in cluster 1 were older than 60 years (34.1%), had a family history of type 2 diabetes mellitus (62.7%), had hypertension (55.4%), routinely took medicines (73.5%), undertook physical activity for less than half an hour (40.5%), and had a high blood pressure level (53.5%). The Pearson correlation test found that age, regular medication use, hypertension and blood pressure level all seem to have significant correlations with cluster outcomes. The risk cluster of type 2 diabetes mellitus was separated into two clusters using Fuzzy C-Means algorithm, namely the high-risk cluster and the low-risk cluster.

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Section: Methodsmentioning

confidence: 99%

The Risk Cluster in Type 2 Diabetes Mellitus Based on Risk Parameters Using Fuzzy C-Means Algorithm

2023

Sci. Technol. Indones

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“…Surya Narayana and Vasumathi proposed new attributes similarity-based K-Medoids clustering technique that achieved better clustering results than traditional K-Medoids algorithm [41]. In their study, Kurniawan et al also compared K-Means and Fuzzy C-Means with another clustering algorithm, linkage, for the NASA active fire dataset and found that Fuzzy C-Means produced better clustering results [42]. On the other hand, Zhou and Yang investigated the effect of cluster size distribution on clustering and compared K-Means and Fuzzy C-Means algorithms [43].…”

Section: Literature Reviewmentioning

confidence: 99%

Clustering of Cities Based on Their Smart Performances: A Comparative Approach of Fuzzy C-Means, K-Means, and K-Medoids

Kenger,

Özceylan

et al. 2023

IEEE Access

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Smart City is recognized as a potential approach to address serious urban issues such as traffic, pollution, energy use, and waste management. Therefore, it is vital to evaluate how smart cities are in order to put these methods into practice. To offer advice on these matters, numerous reports are created, and one of which is Smart City Index (SCI). The Institute for Management Development (IMD) and the Singapore University of Technology and Design collaborate on SCI every year (SUTD). The report evaluates how locals view the buildings and technological applications that are available in their towns. Although the study offers a thorough examination of the cities for evaluation, the city clusters should be more sensitive and not be created using strict clustering techniques. In order to address this problem, the clustering algorithms namely K-Medoids, Fuzzy C-Means, and K-Means, which outperform hard clustering approaches in terms of robustness to vagueness and knowledge retention, are used. The main goal of this study is to categorize cities using a scientific manner (clustering algorithms) based on SCI data and to present how the chosen approaches work for dealing with the associated problems. The primary innovation of the present study is the use of clustering techniques in reports where the indexes are used. The results indicate that grouping the cities on the basis of their smart indicators would not be as effective as using the three clustering techniques that are suggested in this paper. These results add to the analysis of the dynamic capacities of smart cities and highlight the sustainability of these tactics.

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“…Previous research that uses data mining to create clusters in dataset analysis, including the Fuzzy C-Means Algorithm [12]- [14], Multifactor Evaluation Process (MFEP) [15], Comparison of Single Linkage, Complete Linkage and Average Linkage Clustering Methods [16] [14], and K-Means Algorithm [14] [17]by Using Particle Swarm [18]. However, no one has discussed the paper to make groupings know the level of student satisfaction with online learning.…”

Section: Introduction Coronavirus (Covid 19mentioning

confidence: 99%

The Level of Student Satisfaction with the Online Learning Process During a Pandemic Using the K-means Algorithm

Arifin

Natassa

Khoirunnisa

et al. 2021

Inf. J. Ilm. Bid. Teknol. Inf. dan Komun.

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The number of cases of Covid-19 in this pandemic era is increasing and getting out of control every day. This triggers the Indonesian government to set policies on schools with online learning methods. Of course, online learning cannot ensure that it runs smoothly in all circles because several factors hinder the learning process. The difficulty of the internet network, limited quotas, unfamiliarity with the use of learning media, and an unsupportive environment for conducting online learning are the obstacles to ineffective online learning. The purpose of this study was to determine the level of satisfaction with online learning during the pandemic. This study uses quantitative research methods with a descriptive approach. Quantitative research methods will be processed into data mining using the K-Means Clustering Algorithm. The clustering process is carried out to get the results of clustering the level of student satisfaction. The dataset was obtained from the results of the questionnaire by submitting statements of satisfaction and dissatisfaction. The cluster type is based on high, medium, and low class. The test results obtained a value with the final iteration, namely the level of satisfied statements is categorized as high with a value of 11.79 compared to the dissatisfied statement, which is categorized as moderate with a value of 7.46. In contrast, for the low category level, there is no value of 0.00 cluster results state that the category is satisfied with online learning with a value of 9.33.

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Comparison of Clustering K-Means, Fuzzy C-Means, and Linkage for Nasa Active Fire Dataset

Cited by 6 publications

References 11 publications

The Risk Cluster in Type 2 Diabetes Mellitus Based on Risk Parameters Using Fuzzy C-Means Algorithm

The Risk Cluster in Type 2 Diabetes Mellitus Based on Risk Parameters Using Fuzzy C-Means Algorithm

Clustering of Cities Based on Their Smart Performances: A Comparative Approach of Fuzzy C-Means, K-Means, and K-Medoids

The Level of Student Satisfaction with the Online Learning Process During a Pandemic Using the K-means Algorithm

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