A Review on Appropriateness of Partitional Clustering Algorithms in Handling Transactional Data

Abstract

Clustering is an unsupervised learning that widely used in vast researches area. The technique also utilized in any disciplines that involves multivariate data analysis. In term of transactional data handling, the partitional clustering is promoted as the one method to explore knowledge from several attributes that are related the business. In this paper, we investigate the use of partitional clustering algorithms including k-means, k-medoids, Fuzzy C Means, CLARA, and CLARANS. The present article delineates the various stages that are integral to accomplishing a review. These stages encompass data collection, data pre-processing, determination of the number of clusters, implementation of algorithms, and evaluation of clustering. The study pointed out that k-medoids as the most potential to be implemented in handling transactional data. The algorithm has achieved commendable scores in two out of three metrics, namely Calinski-Harabasz Index and Silhouette Index but not for the Davies-Bouldin Index. Nevertheless, k-medoids algorithm emerges as a formidable tool in handling the transactional data and facilitating enhanced decision-making. It is our hope that the knowledge acquired from this research will leading to progress in diverse fields where transactional data holds a crucial position. Keywords: Partitional Clustering, Transactional Data, k-means, k-medoids, Fuzzy C Mean, CLARA, CLARANS