Hierarchical Clustering for Functionalities E-Commerce Adoption

Triandini, Evi; Hermawati, Fajar Astuti; Suniantara, I Ketut Putu

doi:10.21107/kursor.v10i3.230

Cited by 7 publications

(4 citation statements)

References 14 publications

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“…To a lesser extent, there are studies on using other clustering approaches in e-commerce. Hierarchical clustering (of which agglomerative clustering is a subset) in e-commerce applications is discussed [41]. It is worth noting that the standard algorithm for hierarchical agglomerative clustering (HAC) has a time complexity of O(n 3 ) and requires Ω(n 2 ) memory, making it slow even for medium-sized datasets.…”

Section: Clustering Of Usersmentioning

confidence: 99%

Functional Framework for Multivariant E-Commerce User Interfaces

Wasilewski

2024

JTAER

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Modern e-businesses heavily rely on advanced data analytics for product recommendations. However, there are still untapped opportunities to enhance user interfaces. Currently, online stores offer a single-page version to all customers, overlooking individual characteristics. This paper aims to identify the essential components and present a framework for enabling multiple e-commerce user interfaces. It also seeks to address challenges associated with personalized e-commerce user interfaces. The methodology includes detailing the framework for serving diverse e-commerce user interfaces and presenting pilot implementation results. Key components, particularly the role of algorithms in personalizing the user experience, are outlined. The results demonstrate promising outcomes for the implementation of the pilot solution, which caters to various e-commerce user interfaces. User characteristics support multivariant websites, with algorithms facilitating continuous learning. Newly proposed metrics effectively measure changes in user behavior resulting from different interface deployments. This paper underscores the central role of personalized e-commerce user interfaces in optimizing online store efficiency. The framework, supported by machine learning algorithms, showcases the feasibility and benefits of different page versions. The identified components, challenges, and proposed metrics contribute to a comprehensive solution and set the stage for further development of personalized e-commerce interfaces.

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Section: Clustering Of Usersmentioning

confidence: 99%

Functional Framework for Multivariant E-Commerce User Interfaces

Wasilewski

2024

JTAER

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“…Almost all of the methods mentioned were verified for their application for e-commerce solutions. Hierarchical clustering in e-commerce applications is discussed in [18]. It is worth noting that the standard algorithm for hierarchical agglomerative clustering (HAC) can be slow even for medium data sets [1].…”

Section: Related Workmentioning

confidence: 99%

Clusterization methods for multi-variant e-commerce interfaces

Wasilewski

2023

Annals of Computer Science and Information Systems

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E-commerce is a very popular method that lets consumers to purchase goods and services. The ability to purchase items online has increased the need for effective recommendation systems. Such recommendations usually relete to products in which the customer may be interested. However, there are wider opportunities to tailor e-commerce to individual customer needs and behaviour. In this paper, the architecture of the e-commerce platform (named AIM 2 ), which allows providing a dedicated interface to selected user groups, is discussed. A key component of the platform is the module responsible for dividing customers into groups, using selected clustering methods. Each of the implemented methods can be parameterised to adapt the customer segmentation to a given e-commerce business owner's requirements. This article describes the results of an analysis of the impact of selected methods and parameters on clustering results. Moreover, it identifies key metrics that should be considered when selecting clustering conditions during the implementation of the platform. Finally, the main results of the pilot implementation of AIM 2 are presented to assess the effectiveness of the multi-variant user interface.

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“…Metode clustering dibedakan menjadi dua yaitu partitioning dan hierarchical [15]. Yang termasuk dalam partitioning diantaranya adalah k-means clustering dan fuzzy c-means clustering.…”

Section: Pendahuluanunclassified

Untitled

2022

KONV

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The problem of nutritional status in toddlers is still a major problem that needs attention, one of which is malnutrition. Malnutrition problems are directly caused by inadequate food intake, inadequate family economic factors, and caused by underlying diseases such as tuberculosis, heart disease or birth defects. The purpose of this study is to implement the clustering method with the K-Means and Fuzzy C-Means algorithms which aims to evaluate the nutritional status of children under five in general so that it can be used as a basis for early prevention for health workers at the puskesmas to overcome malnutrition. This study uses 4 parameters, namely height, weight, genderand age and uses a silhouette index validation calculation to measure the resulting cluster cohesiveness. From the result of the cluster analysis, the K-Means algorithm gets a validation result of 0.79 with 4 clusters and the Fuzzy C-Means algorithm gets a validation result of 0.78 with 4 clusters. From the two comparison algorithm, the K-Means algorithm gets the best validation because get the highest validation compared to the Fuzzy C-Means algorithm.

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Hierarchical Clustering for Functionalities E-Commerce Adoption

Cited by 7 publications

References 14 publications

Functional Framework for Multivariant E-Commerce User Interfaces

Functional Framework for Multivariant E-Commerce User Interfaces

Clusterization methods for multi-variant e-commerce interfaces

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