The Data Mining Approach: A Case Study - Clustering Algorithms for After Sales Service

Abstract

Data mining is a business-effective technology to provide customer experience enhancement and alleviate the process of decision-making along the digital transformation journey. The main goal of this research paper is to provide a case study - an analysis on the implementation of data mining techniques, in particular clustering techniques, and a theoretical analysis and research of the clustering algorithms such as SimpleKMeans, MakeDensity BasedClusterer, Canopy, FarthestFirst and FilteredClusterer. The benefit of this practical comparative study of various clustering algorithms, by using the Weka tool, helps especially with analyzing information and predicting repairs and installations in home appliances after sales services business. Accordingly, the findings prove the impact of clustering techniques on decision-making and encourage businesses to adapt the data mining approach along their business digital transformation journey.