<![CDATA[Facing large amounts of high-dimensional transaction data, clustering approaches often face challenges that include elasticity, weak high-dimensional data processing capabilities, sensitivity to data order over time, independence from parameters, and the ability to manage noise. These problems can limit a method from producing accurate predictions. Experiments conducted with data samples collected from 50 different mobile phones purchased on Lazada yielded the following results: K-means outperforms Single-pass in evaluating e-commerce transactions because it has higher intra-class dissimilarity and inter-class similarity. K-means clustering is an approach to the effective and flexible organization of large datasets. The results of a clustering algorithm are sensitive not only to the total number of clusters but also to how they were originally arranged. Therefore, it is easy to show that the clustering results are locally optimized. Further research conducted into the elements that influence the number of clusters produced by this method as well as the initial location of clustering centers is a very important endeavor.]]>
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