Initial seed selection for K-modes clustering – A distance and density based approach

Sajidha, S. A.; Desikan, Kalyani

doi:10.1016/j.jksuci.2018.04.013

Cited by 9 publications

(5 citation statements)

References 9 publications

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“…The dissimilarity measure applied in K-Means algorithm is the reason that K-Means is unable to cluster categorical variables [29]. K-Modes clustering algorithm is introduced by Huang [30] by presenting a new measurement of dissimilarity to cluster categorical attributes [31]. While maintaining its proficiency, K-Modes clustering model eliminates the numeric data restriction.…”

Section: B Customer Segmentation Via Clustering Methodsmentioning

confidence: 99%

Customer Segmentation and Profiling for Life Insurance using K-Modes Clustering and Decision Tree Classifier

Abdul-Rahman¹,

Arifin²,

Hanafiah³

et al. 2021

IJACSA

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Customer segmentation and profiling has become an important marketing strategy in most businesses as a preparation for better customer services as well as enhancing customer relationship management. This study presents the segmentation and classification technique for insurance industry via data mining approaches: K-Modes Clustering and Decision Tree Classifier. Data from an insurance company were gathered. Decision Tree Algorithm was applied for customer profile classification comparing two methods which are Entropy and Gini. K-Modes Clustering segmentized the customers into three prominent groups which are "Potential High-Value Customers", "Low Value Customers" and "Disinterested Customers". Decision Tree with Gini model with 10-fold cross validation was found as the best fit model with average accuracy of 81.30%. This segmentation would help marketing team of insurance company to strategize their marketing plans based on different group of customers by formulating different approaches to maximize customer values. Customers can receive customization of insurance plans which satisfy their necessity as well as better assistance or services from insurance companies.

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Section: B Customer Segmentation Via Clustering Methodsmentioning

confidence: 99%

Customer Segmentation and Profiling for Life Insurance using K-Modes Clustering and Decision Tree Classifier

Abdul-Rahman¹,

Arifin²,

Hanafiah³

et al. 2021

IJACSA

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show abstract

“…Selecting representative seed points (Manochandar et al., 2020; Sajidha et al., 2018) has a crucial impact on the accuracy of clustering algorithm. We counted the frequency of effective pixel points after dimension‐reduction (Formula ), and took m coordinate points ( G m − R m , G m − B m ) with higher frequency as candidate clustering seed points, whose priority depended on the frequency (Kumar & Reddy, 2017).…”

Section: Methodsmentioning

confidence: 99%

A New Clustering Algorithm Toward Building Segmentation From Aerial Images by Utilizing RGB‐Component Differences

Yang

Liu

et al. 2021

Earth and Space Science

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“…The K-modes algorithm has several additional shortcomings. For example, inability to detect the number of clusters, inability to converge to the global optimum, and prone to outliers [4,47].…”

Section: K-modes Clusteringmentioning

confidence: 99%

Applications of Clustering Methods for Different Aspects of Electric Vehicles

2023

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The growing penetration of electric vehicles can pose several challenges for power systems, especially distribution systems, due to the introduction of significant uncertain load. Analysis of these challenges becomes computationally expensive with higher penetration of electric vehicles due to various preferences, travel behavior, and the battery size of electric vehicles. This problem can be addressed using clustering methods which have been successfully used in many other sectors. Recently, there have been several studies published on applying clustering methods for various aspects of electric vehicles. To summarize the existing efforts and provide future research directions, this contribution presents a three-step analysis. First, the existing clustering methods, including hard and soft clustering, are discussed. Then, the recent literature on the application of clustering methods for different aspects of electric vehicles is reviewed. The review concentrates on four major aspects of electric vehicles: the behavior of the user, driving cycle, used batteries, and charging stations. Then, several representative studies are selected from each category and their merits and demerits are summarized. Finally, gaps in the existing literature are identified and directions for future research are presented. They indicate the need for further research on the impact on distribution circuits, charging infrastructure during emergencies, equity and disparity in rebate allocations, and the use of big data with cluster analysis to assist transportation network management.

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Initial seed selection for K-modes clustering – A distance and density based approach

Cited by 9 publications

References 9 publications

Customer Segmentation and Profiling for Life Insurance using K-Modes Clustering and Decision Tree Classifier

Customer Segmentation and Profiling for Life Insurance using K-Modes Clustering and Decision Tree Classifier

A New Clustering Algorithm Toward Building Segmentation From Aerial Images by Utilizing RGB‐Component Differences

Applications of Clustering Methods for Different Aspects of Electric Vehicles

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