Comparison Between K-Means and Fuzzy C-Means Clustering in Network Traffic Activities

Purnawansyah,; Haviluddin, .; Gafar, Achmad Fanany Onnilita; Tahyudin, Imam

doi:10.1007/978-3-319-59280-0_24

Cited by 19 publications

(11 citation statements)

References 9 publications

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“…The iteration process is done continuously until no change of cluster membership in each cluster. The distance function used is Euclidean distance [43][44][45][46]. The K-Mean clustering algorithm is shown in Fig.…”

Section: K-mean Clusteringmentioning

confidence: 99%

A Performance of the Scattered Averaging Technique based on the Dataset for the Cluster Center Initialization

Putra¹,

Gaffar²,

Suprapty³

et al. 2021

IJMECS

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Clustering is one of the primary functions in data mining explorations and statistical data analysis which widely used in various fields. There are two types of the clustering algorithms which try to optimize certain objective function, i.e. the hierarchical and partitional clustering. This study focuses on the achievement of the best cluster results of the hard and soft clustering (K-Mean, FCM, and SOM clustering). The validation index called GOS (Global Optimum Solution) used to evaluate the cluster results. GOS index defined as a ratio of the distance variance within a cluster to the distance variance between clusters. The aim of this study is to produce the best GOS index through the use of the proposed method called the scattered averaging technique based on datasets for the cluster center initialization. The cluster results of each algorithm are also compared to determine the best GOS index between them. By using the annual rainfall data as the dataset, the results of this study showed that the proposed method significantly improved K-Mean clustering ability to achieve the global optimum solution with a performance ratio of 69.05% of the total performance of the three algorithms. The next best clustering algorithm is SOM clustering (24.65%) followed by FCM clustering (6.30%). In addition, the results of this study also showed that the three clustering algorithms achieve their best global optimum solution at the number of even clusters.

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Section: K-mean Clusteringmentioning

confidence: 99%

A Performance of the Scattered Averaging Technique based on the Dataset for the Cluster Center Initialization

Putra¹,

Gaffar²,

Suprapty³

et al. 2021

IJMECS

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“…Aktifitas klasterisasi membutuhkan sebuah algoritme untuk menyelesaikan tugasnya. Beberapa variasi algoritme yang digunakan untuk membuat klaster di antaranya adalah k-means [8]- [11] dan fuzzy c-means (FCM) [12]- [17].…”

Section: Pendahuluanunclassified

“…Algoritme bisecting k-means menghasilkan nilai koefisien silhouette yang paling tinggi atau mempunyai kualitas klaster lebih baik daripada k-means seperti yang digunakan dalam [8]- [11]. Keunggulan bisecting kmeans ini sesuai dengan [20] yang mengukur perbandingan algoritme menggunakan parameter entropy, f-measure, dan overall similarity.…”

Section: X= ( P −M I N )×( M a X N E W −M I N Ne W ) (M A X−m I N )+M I N Ne W (2)unclassified

Customer segmentation using bisecting k-means algorithm based on recency, frequency, and monetary (RFM) model

Puspitasari

Widians

Setiawan

2019

Jurnal Teknologi dan Sistem Komputer

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Information on customer loyalty characteristics in a company is needed to improve service to customers. A customer segmentation model based on transaction data can provide this information. This study used parameters from the recency, frequency, and monetary (RFM) model in determining customer segmentation and bisecting k-means algorithm to determine the number of clusters. The dataset used 588 sales transactions for PT Dinar Energi Utama in 2017. The clusters formed by the bisecting k-means and k-means algorithm were tested using the silhouette coefficient method. The bisecting k-means algorithm can form the best customer segmentation into three groups, namely Occasional, Typical, and Gold, with a silhouette coefficient of 0.58132.

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“…The utility of fuzzy sets lies in their ability to model uncertain or ambiguous data. Fuzziness in a fuzzy set is characterized by its Membership Functions [9], [10], [12], [13] …”

Section: Fuzzy Logicmentioning

confidence: 99%

Deflection prediction of cantilever concrete beam using adaptive fuzzy inference system

Jepriani¹,

Ibayasid²,

Wibawa³

et al. 2018

IJET

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The cantilever concrete beam is a beam made of concrete that is only supported or clamped at one end and the other end free or without the pedestal. The measure to which a structural member gets deviated from the initial position is called deflection. All the internal forces of cantilever beam serve to hold all the external forces due to the load so that the structure remains balanced. While the beam gets deflected under the loads, bending occurs in the same plane due to which stresses are developed. From the moment balance formula after integrated and solved with required boundary conditions, we get the downward deflection of beam. Fuzzy logic provides an inference structure that enables appropriate human reasoning capabilities. FIS (Fuzzy Inference System) is a system that processes the mapping formulation of a given input to produce an output using Fuzzy Logic. By using randomized data for all the variables of deflection formula within their respective ranges as training data, the FIS will be able to predict deflection of cantilever concrete beam after going through the training process adaptively.

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Comparison Between K-Means and Fuzzy C-Means Clustering in Network Traffic Activities

Cited by 19 publications

References 9 publications

A Performance of the Scattered Averaging Technique based on the Dataset for the Cluster Center Initialization

A Performance of the Scattered Averaging Technique based on the Dataset for the Cluster Center Initialization

Customer segmentation using bisecting k-means algorithm based on recency, frequency, and monetary (RFM) model

Deflection prediction of cantilever concrete beam using adaptive fuzzy inference system

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