Call detail record-based traffic density analysis using global K-means clustering

Nair, Suja Chandrasekharan; Elayidom, M. Sudheep; Gopalan, Sasi

doi:10.1504/ijie.2020.104654

Cited by 10 publications

(7 citation statements)

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“…The most common clustering algorithms [43] used in mobile network data analysis are density-based [44][45][46], hierarchical [47][48][49] and partitional clustering [35,46,50,51]. Densitybased clustering aims to construct groups based on low and high-density regions.…”

Section: Clusteringmentioning

confidence: 99%

Do Social Event Attendees cluster based on Socioeconomic Status?

Szabó¹,

Pintér²,

Felde³

2023

Preprint

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Mobile phones have become an integral part of our lives in the last two decades, leaving a digital trace of our activities and communication. This study aims to develop a data processing framework to evaluate human mobility and socioeconomic status based on call detail records. The methodology proposed first calculates radius of gyration and entropy for each user, then estimates the socioeconomic status by the price and age of the subscribers' phones. Finally, an unsupervised machine learning algorithm was used to group the cells into clusters based on their mobility and socioeconomic metrics. The research showed differences between Buda and Pest during a large scale social event using mobile phone ages and prices. Additionally, the clustering results revealed homogenous groups of cells around Budapest, with similar mobility and socioeconomic metrics. The main conclusion is that mobile network data combined with mobile phone properties offer a useful tool for characterising urban mobility and socioeconomic status.

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Section: Clusteringmentioning

confidence: 99%

Do Social Event Attendees cluster based on Socioeconomic Status?

Szabó¹,

Pintér²,

Felde³

2023

Preprint

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“…K-means can typically be applied to data that has a smaller number of dimensions, is numeric, and is contin-uous. Following is a list of some interesting use cases for k-means [11] In order to detect frauds ,Telecom companies [12] use Call Detail Record which contains information of call, SMS, and Internet activity of a customer, in order to detect fraud detection by clustering the user profiles, reducing customer churn by usage activity.The customer activities for 24 hours were clustered using k means algorithm.…”

Section: Motivationmentioning

confidence: 99%

A Comparison of K-Means and Mean Shift Algorithms

Shumaila¹

2021

Preprint

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Clustering, or otherwise known as cluster analysis, is a learning problem that takes place without any human supervision. This technique has often been utilized, much efficiently, in data analysis, and serves for observing and identifying interesting, useful, or desired patterns in the said data. The clustering technique functions by performing a structured division of the data involved, in similar objects based on the characteristics that it identifies. This process results in the formation of groups, and each group that is formed, is called a cluster. A single said cluster consists of objects from the data, that have similarities among other objects found in the same cluster, and resemble differences when compared to objects identified from the data that now exist in other clusters. The process of clustering is very significant in various aspects of data analysis, as it determines and presents the intrinsic grouping of objects present in the data, based on their attributes, in a batch of unlabeled raw data. A textbook or otherwise said, good criteria, does not exist in this method of cluster analysis. That is because this process is so different and so customizable for every user, that needs it in his/her various and different needs. There is no outright best clustering algorithm, as it massively depends on the user’s scenario and needs. This paper is intended to compare and study two different clustering algorithms. The algorithms under investigation are k-mean and mean shift. These algorithms are compared according to the following factors: time complexity, training, prediction performance and accuracy of the clustering algorithms.

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“…Fantasy league stat analysis 6. Insurance Fraud Detection In order to detect frauds, Telecom companies [13] use Call Detail Record which contains information of call, SMS, and Internet activity of a customer, in order to detect fraud detection by clustering the user profiles, reducing customer churn by usage activity. The customer activities for 24 hours were clustered using k means algorithm.…”

Section: Motivationmentioning

confidence: 99%

A Comparison of K-Means and Mean Shift Algorithms

Shumaila¹

2021

IJTAM

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Clustering, also known as cluster analysis, is a learning problem that occurs without the intervention of a human. This technique is frequently used very efficiently in data analysis to observe and identify interesting, useful, or desirable patterns in data. The clustering technique operates by dividing the data involved into similar objects based on their identified properties. This process results in the formation of groups, and each formed group is referred to as a cluster. A single said cluster consists of objects from the data that share similarities with other objects found in the same cluster and differ from objects identified from the data that now exist in other clusters. Clustering is an important process in many aspects of data analysis because it determines and presents the intrinsic grouping of objects in the data based on their attributes in a batch of unlabeled raw data. This method of cluster analysis lacks a textbook or, to put it another way, good criteria. This is due to the fact that this process is unique and customizable for each user who requires it for a variety of reasons. There is no single best clustering algorithm because it is so dependent on the user's scenario and needs. The purpose of this paper is to compare and contrast two different clustering algorithms. The algorithms under consideration are the k-mean and the mean shift. These algorithms are compared based on the following criteria: time complexity, training, prediction performance, and clustering algorithm accuracy.

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Call detail record-based traffic density analysis using global K-means clustering

Cited by 10 publications

References 0 publications

Do Social Event Attendees cluster based on Socioeconomic Status?

Do Social Event Attendees cluster based on Socioeconomic Status?

A Comparison of K-Means and Mean Shift Algorithms

A Comparison of K-Means and Mean Shift Algorithms

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