K-Means cluster analysis in earthquake epicenter clustering

Pepi, Novianti; Setyorini, Dyah; Rafflesia, Ulfasari

doi:10.26555/ijain.v3i2.100

Cited by 40 publications

(30 citation statements)

References 14 publications

(16 reference statements)

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“…K-mean clustering belongs to a hard partitioning algorithm, which divides the multi-attribute data set into a number of K clusters [5]. Each individual in the dataset is allocated entirely to a particular cluster based on the closest distance to its centroid.…”

Section: K-meanmentioning

confidence: 99%

“…K-Mean clustering is the most primitive, simple and effective method. This method has been used to mining the attitude of social network users and earthquake epicenter clustering in [4,5].The only drawback is in the inconsistency of members of each cluster when new centroid initialization is used. Research on improving K-Mean ability has been done in [6].…”

Section: Introductionmentioning

confidence: 99%

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Assessment of the Average Level of TOEFL Score by using SOM (Self Organizing Map) and K-Mean Clustering Techniques

2019

IJRTE

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Economic growth as measured by GDP growth rates and economic growth set as an increase in GDP strongly helps government predictions about the economic situation and the formation of economic development strategies. This measurement is done by combining mathematical and computer technology to make qualitative and quantitative predictions scientifically and appropriately for economic growth trends. It is a good practical sense to use scientific and proven methods to predict future GDP development trends of a particular economy. In some cases, machine learning methods have proven to be better forecasting results than statistical methods. A Deep Neural Network (DNN) is one type of ANN (Artificial Neural network) architecture based on deep MLP (Multi Layer Perceptron), which uses Deep Learning training techniques. This study proposes the use of DNN to predict the percentage of GDP distribution at current prices by industry sector. In this case, the DNN used will have multiple outputs as many industry sectors. The aim of this study is how to predict for the next period with the smallest possible prediction errors by using DNN.

show abstract

Section: K-meanmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assessment of the Average Level of TOEFL Score by using SOM (Self Organizing Map) and K-Mean Clustering Techniques

2019

IJRTE

View full text Add to dashboard Cite

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“…The relationship of between its datasets and the center of its cluster is the shortest distance. This algorithm has been used to cluster the earthquake epicenter in [4], to mining the attitude of social network users in [5], and mapping of image and video in [6]. The uniform effects of this algorithm have studied in [7].…”

Section: Introductionmentioning

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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“…Evolving clustering is an essential data analysis topic for a wide range of applications, such as the following: evolving clusters can be generated in forecasting weather conditions [4,[35][36][37], in earthquake forecasting software based on the analysis of different sources of data from the Earth [38,39], in intelligent transportation systems for traffic congestion prediction in smart cities [40], in chemistry for forecasting the results of molecular interactions [41], in network intrusion detections (NIDS) [15,42,43], in various software performs, and in predicting stock increases or decreases based on their relations with different time series factors [44].…”

Section: Introductionmentioning

confidence: 99%

Online Clustering of Evolving Data Streams Using a Density Grid-Based Method

et al. 2020

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In recent years, a significant boost in data availability for persistent data streams has been observed. These data streams are continually evolving, with the clusters frequently forming arbitrary shapes instead of regular shapes in the data space. This characteristic leads to an exponential increase in the processing time of traditional clustering algorithms for data streams. In this study, we propose a new online method, which is a density grid-based method for data stream clustering. The primary objectives of the density grid-based method are to reduce the number of distant function calls and to improve the cluster quality. The method is conducted entirely online and consists of two main phases. The first phase generates the Core Micro-Clusters (CMCs), and the second phase combines the CMCs into macro clusters. The gridbased method was utilized as an outlier buffer in order to handle multi-density data and noises. The method was tested on real and synthetic data streams employing different quality metrics and was compared with the popular method of clustering evolving data streams into arbitrary shapes. The proposed method was demonstrated to be an effective solution for reducing the number of calls to the distance function and improving the cluster quality. INDEX TERMS Clustering, data stream, evolving, grid-based method, core-micro-cluster, online.

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K-Means cluster analysis in earthquake epicenter clustering

Cited by 40 publications

References 14 publications

Assessment of the Average Level of TOEFL Score by using SOM (Self Organizing Map) and K-Mean Clustering Techniques

Assessment of the Average Level of TOEFL Score by using SOM (Self Organizing Map) and K-Mean Clustering Techniques

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

Online Clustering of Evolving Data Streams Using a Density Grid-Based Method

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