A Comparative Survey on K-Means and Hierarchical Clustering in E-Commerce Systems

Reddy, Chinnam Sasidhar; Rao, Narasimha; Sisir, Atkuri; Raju, Vysyaraju Shanmukha Srinivasa; Aravinth, S. S.

doi:10.1109/idciot56793.2023.10053472

Cited by 10 publications

(1 citation statement)

References 3 publications

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“…Data mining employs two distinct clustering techniques for data grouping [14], [15], [16]. Delivery services are essential to e-commerce because they make it simple for individuals and businesses to send goods [7]. In addition, even when sales and purchases do not immediately coincide, it can facilitate shopping.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Promotional Strategy Mapping Using the K-Means Clustering Algorithm to Raise Sales

Fathurrozi,

Ginanjar Laksana

2024

aisthebest

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To enhance sales, organizations must improve the alignment of their promotional tactics. Enterprises have the ability to promote their goods in locations where there is demand for them. Facilitating the delivery of the goods would enhance the ease with which clients can carry out their purchases and sales transactions. A corporation's ability to strategically allocate its goods enables it to expand its operations. Prospective clients have a greater array of choices at their disposal than the total number of enterprises operating within the same sector. This is accomplished by using a diverse range of promotional media to enhance the sales of products and services. Optimizing promotional strategies is the first and critical stage in presenting items to clients, as it directly impacts the benefits that the firm will get. So far, the sales process has not been affected by the promotional method. The objective of this research was to use the K-Means Clustering algorithm in a data mining procedure to optimize the categorization of customer data, CRISP-DM is used for the purpose of comprehending and preparing data, constructing models, evaluating them, and deploying them. The CRISP-DM method is employed specifically for the construction of clusters. A non-hierarchical clustering technique called K-Means divides data into many groups according on how similar they are. The program facilitates the determination of appropriate location mapping for promotional purposes. The study results may serve as a foundation for decision-making in order to maximize promotional techniques, using the generated clusters.

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