The impact of e-commerce and the ever-increasing integration of the online world to the daily lives of citizens are the driving reasons for the creation of this thesis. To help streamline the user’s shopping experience, the E-Commerce Cheapest Clusterer (ECCC) achieves this goal by doing its namesake: to group similar products into corresponding clusters and select the most affordable product in each of the clusters. The result is then displayed in an easy-to-browse interface that directly connects to the project’s target website, Lazada. The images are compared using the Structural Similarity Index Measure (SSIM). If the value returned by the SSIM function is greater than a certain threshold, the image enters the same cluster as the one it was compared to. To ensure efficiency, this paper also investigates the results of different threshold values. Setting a lower threshold value resulted in the algorithm finishing faster at the cost of several misplaced images in clusters, whereas a higher threshold value gave the program a considerable runtime but was less prone to misidentification. Additionally, the testing also revealed that setting the threshold value too high can hinder the program’s ability to cluster similar images even if the difference is slight.