Analyzing the Performance of ECLAT Algorithm for Large Datasets by Comparing K-means and Gaussian Mixture Model

Abstract

Frequent Itemset Mining (FIM) is a technique that transforms historical data into useful information by identifying beneficial patterns. The ECLAT method uses depth-first search to intersect the transaction ID sets with the corresponding k^th item sets in order to calculate the support items. While searching for the best-selling products, ECLAT uses a lot of memory and processing time due to the enormous number of transaction ID sets. To overcome these problems, the clustering method combines with the ECLAT algorithm to retrieve the support items. Description elements 100,000 to 400,000 were used to retrieve the support items of the most popular selling goods. For the K-means clustering approach, the optimal value of k is 8 clusters according to the 0.59 silhouette value. For the Gaussian Mixture Model, the ideal value of k is 14 clusters based on a 0.59 silhouette score value between 100,000 and 400,000 data items. After clustering the same product items, the ECLAT algorithm retrieves the support items by applying a minimum support value of 0.00001 in this investigation. According to the experimental results, the Gaussian Mixture Model not only offers more flexibility for clustering the same items but also reduces the memory usage and execution times. The outcomes of this investigation indicate that the Gaussian Mixture Model provides more efficient enhancement of the performance of the ECLAT algorithm than the K-means algorithm.