A Proposed Modification of K-Means Algorithm

Mahmood, Sharfuddin; Rahaman, Mohammad Saiedur; Nandi, Dip; Rahman, Mohd Nordin Abdul

doi:10.5815/ijmecs.2015.06.06

Cited by 5 publications

(2 citation statements)

References 10 publications

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“…Baru-baru ini, teknik K-means telah berhasil diterapkan [12]. K-means adalah teknik berbasis partisi yang membagi data menjadi set berdasarkan kedekatan mereka satu sama lain [13]. Teknik Kmeans asli [14] menerapkan jarak Euclidean untuk menduplikasi pola perkiraan kemiripan antara titik data: metode yang tidak cocok untuk banyak aplikasi.…”

Section: Pendahuluanunclassified

Pendekatan Clustering untuk Menganalisis Efisiensi dan Kinerja Mahasiswa Berdasarkan Data Menerapkan Metode K-Means

Rahmadhani

2022

mib

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The purpose of this study is to cluster the efficiency and performance of students. This is because the academic community is currently faced with several challenges in terms of analyzing and evaluating the progress of a student's academic achievement. In the real world, classifying student performance is a scientifically challenging task. Recently, several studies have applied cluster analysis to evaluate student outcomes and used statistical techniques to divide their scores in relation to student performance. This approach, however, is not efficient. In this study, we combined two techniques, namely k-mean and elbow clustering algorithm to evaluate student performance. Based on this combination, the performance results will be more accurate in analyzing and evaluating the progress of student performance, the application of the Elbow method according to this study gives the best number of clusters to 3, and when the K-Means method is applied, data is generated that the number of students is 73 students, from 4 repetitions. There are 3 clusters, namely the category of "Achievable", "Potential for Achievement", and "Less Achievement", with the results of the "Achievable" cluster as many as 34 students with a percentage of 47.22%, the cluster "Potential for Achievement" as many as 24 students with a percentage of 33.33 %, and the "Less Achievement" cluster as many as 15 students with a percentage of 19.45%.

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Section: Pendahuluanunclassified

Pendekatan Clustering untuk Menganalisis Efisiensi dan Kinerja Mahasiswa Berdasarkan Data Menerapkan Metode K-Means

Rahmadhani

2022

mib

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“…In the paper [14], authors present new centroids initialization approach to improving the basic k-means algorithm with high-quality clusters. Authors in papers [22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40] have tried to improve the clustering algorithms which are used in various domains like networking and biometrics. However, these algorithms can be improved further.…”

Section: Related Workmentioning

confidence: 99%

Performance Analysis of Improved Clustering Algorithm on Real and Synthetic Data

Khandare¹,

Alvi²

2017

IJCNIS

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Abstract-Clustering is an important technique in data mining to partition the data objects into clusters. It is a way to generate groups from the data objects. Different data clustering methods or algorithms are discussed in the various literature. Some of these are efficient while some are inefficient for large data. The k-means, Partition Around Method (PAM) or k-medoids, hierarchical and DBSCAN are various clustering algorithms. The k-means algorithm is more popular than the other algorithms used to partition data into k clusters. For this algorithm, k should be provided explicitly. Also, initial means are taken randomly but this may generate clusters with poor quality. This paper is a study and implementation of an improved clustering algorithm which automatically predicts the value of k and uses a new technique to take initial means. The performance analysis of the improved algorithm and other algorithms by using real and dummy datasets is presented in this paper. To measure the performance of algorithms, this paper uses running time of algorithms and various cluster validity measures. Cluster validity measures include sum squared error, silhouette score, compactness, separation, Dunn index and DB index. Also, the k predicted by the improved algorithm is compared with optimal k suggested by elbow method. It is found that both values of k are almost similar. Most of the values of validity measures for the improved algorithm are found to be optimal.

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Research on Logistics Center Location-Allocation Problem Based on Two-Stage K-Means Algorithms

Wang

Wei

2020

Advances in Computer Science for Engineering and Education III

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A Proposed Modification of K-Means Algorithm

Cited by 5 publications

References 10 publications

Pendekatan Clustering untuk Menganalisis Efisiensi dan Kinerja Mahasiswa Berdasarkan Data Menerapkan Metode K-Means

Pendekatan Clustering untuk Menganalisis Efisiensi dan Kinerja Mahasiswa Berdasarkan Data Menerapkan Metode K-Means

Performance Analysis of Improved Clustering Algorithm on Real and Synthetic Data

Research on Logistics Center Location-Allocation Problem Based on Two-Stage K-Means Algorithms

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