Distance Functions Study in Fuzzy C-Means  Core and Reduct Clustering

Eliyanto, Joko; Surono, Sugiyarto

doi:10.26555/jiteki.v7i1.20516

Cited by 4 publications

(3 citation statements)

References 19 publications

(29 reference statements)

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“…In K-means the quality of the grouping results can run consistently in all the distance functions tested. The distance with the best evaluation result is the Euclidean distance (Eliyanto & Surono, 2021). Use of K-Means algorithm is very sensitive to initialize the cluster center because it is already done randomly and using the mean value as the center of the cluster (Sembiring Brahmana et al, 2020).…”

Section: Methodsmentioning

confidence: 99%

Superior Class to Improve Student Achievement Using the K-Means Algorithm

Syahputra¹,

Hutagalung²

2022

SinkrOn

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The accumulation of new student data every year makes searching and processing data difficult, including selecting superior class students according to their talents and abilities. Therefore, the application of the K-Means Clustering data mining method is carried out to support decisions in grouping superior classes. The report card values for each class were used as parameters with a data sample of 80 students and 3 clusters were taken which then resulted in the selection and distribution of superior classes. The purpose of the study was to classify students in the superior class so that they could improve student achievement at SMK Raksana 2 Medan. Results Based on the calculation of the variable distance at the initial centroid with a sample of 80 students and the third iteration, the WCV value is 360.9745 and the BCV value is 7.3575 with a ratio value of 0.0203. Each cluster, namely: Cluster 1 has 43 students including the superior class category. Cluster 2 has 18 students and Cluster 3 has 19 students. Clusters 2 and 3 are included in the regular class category with a total of 37 students. The web-based K-Means application can provide information and solutions needed by schools to classify and determine superior classes so that they can improve student achievement in schools. These results can be used by the school to analyze student achievement and can assist teachers in forming superior classes so as to motivate students to study harder.

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Section: Methodsmentioning

confidence: 99%

Superior Class to Improve Student Achievement Using the K-Means Algorithm

Syahputra¹,

Hutagalung²

2022

SinkrOn

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“…In this research, data that have a high correlation are assumed to have similarities. In the original K-Means algorithm, the similarity is obtained by calculating the Euclidian distance [29]. In the proposed algorithm, the distance calculation is obtained by considering the correlation between attributes.…”

Section: K-means Algorithm Modificationmentioning

confidence: 99%

“…Several distance calculation methods that have been carried out by researchers are Euclidian, Manhattan, Chebyshev, Minkowski, etc. [29][30] [31]. One modification of the distance calculation in K-Means that considers correlation is by calculating the Mahalanobis distance [32], but what is used is the covariance value, not the pure correlation value.…”

Section: Introductionmentioning

confidence: 99%

Proposed Modification of K-Means Clustering Algorithm with Distance Calculation Based on Correlation

Rachmatullah

2022

Jurnal Ilmiah Teknik Elektro Komputer Dan Informatika

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Clustering is a technique in data mining that groups a set of data into groups (clusters) of similar data. In general, there are two methods of clustering, namely the hierarchical method and the partition method. One of the most commonly used partition clustering methods in clustering is K-Means. The use of K-means method has been widely used in various fields with various purposes. Many research has been carried out to improve the performance of the K-Means method, for example, by modifying the method of determining the initial centroid or determining the appropriate number of clusters. In this research, the modification of the K-Means algorithm was carried out in calculating the distance by considering the correlation value between attributes. Attributes that have a high correlation value are assumed to have similar characteristics so that they determine the location of data in a particular cluster. The steps of the proposed method are: calculating the correlation value between attributes, determining the cluster centroid, calculating the distance by considering the value of correlation, and determining the data into certain clusters. The first contribution of this research is to propose a new distance calculation technique in the K-Means algorithm by considering correlation and the second contribution is to apply the proposed algorithm to a specific dataset, namely Iris dataset. In this research, the performance calculation of the modified algorithm was also carried out. From the experimental results using the Iris dataset, the proposed modification of the K-Means algorithm has fewer iterations than the original K-Means method, so that it requires less processing time. The original K-Means method requires 8 iterations, while the proposed method requires only 6 iterations. The proposed method also produces a higher accuracy rate of 89.33% than the original K-Means method, which is 82.67%.

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