The Application of K-means Clustering Algorithm in the Evaluation of E-Commerce Websites

When dealing with large amounts of high-dimensional transaction data, clustering approaches often struggle with challenges including elasticity, weak processing capabilities of high-dimensional data, sensitivity to data sequence across time, independence of parameters, and ability to manage noise. These problems may prevent the methods from providing accurate predictions. Experiments conducted with data samples collected from 300 different mobile phones purchased on Taobao yielded the following results. K-means beats Single-pass in evaluating e-commerce transactions because of its higher intra-class dissimilarity and inter-class similarity. K-means clustering is an approach to the organization of massive datasets that is both effective and flexible. The outcome of the clustering algorithm is sensitive not only to the total number of clusters but also to how they were initially arranged. Because of this, it is simple to demonstrate that the results of clustering are best optimized locally. For this reason, continuing research into the elements that influence the number of clusters produced by this method as well as the starting locations of the clustering center is a crucial endeavor.