Analysis of K-Means Clustering Algorithm: A Case Study Using Large Scale E-Commerce Products

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Abstract:

E-commerce has become a crucial platform consists a large database of products with billions number of retailers and consumers. 

However, these products are placed into different categories according to the structure of different websites. 

A clustering analysis using K-Means Clustering algorithm helps in providing an insightful pattern on categories of clustered products. 

This analysis leads to an automatic classification model to classify the products efficiently. 

This paper presents a step by step cluster analysis using K-Means clustering to group e-commerce products from the online store website in Malaysia. 

The results show that the e-commerce products were categorized into three clusters. 

The most frequent words in each cluster provided a useful insight on the category of the clustered products which were hair and face, oral and pets care products. 

Hence, K-Means clustering analysis able to group a large data set of e-commerce products effectively.

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https://ieeexplore.ieee.org/document/8987140

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[conf]

[k-means]

[clustering]