Analysis of Unsupervised Machine Learning Techniques for an Efficient Customer Segmentation using Clustering Ensemble and Spectral Clustering

Abstract

Customer segmentation is key to a corporate decision support system. It is an important marketing technique that can target specific client categories. We create a novel consumer segmentation technique based on a clustering ensemble; in this stage, we ensemble four fundamental clustering models: DBSCAN, K-means, Mini Batch K-means, and Mean Shift, to deliver a consistent and high-quality conclusion. Then, we use spectral clustering to integrate numerous clustering findings and increase clustering quality. The new technique is more flexible with client data. Feature engineering cleans, processes, and transforms raw data into features. These traits are then used to form clusters. Adjust Rand Index (ARI), Normalized Mutual Information (NMI), Dunn's Index (DI), and Silhouette Coefficient (SC) were utilized to evaluate our model's performances with individual clustering approaches. The experimental analysis found that our model has the best ARI (70.14%), NMI (71.75), DI (75.15), and SC (72.89%). After retaining these results, we applied our model to an actual dataset obtained from Moroccan citizens via social networks and email boxes between 03/06/2022 and 19/08/2022.

An Exploration of Clustering Algorithms for Customer Segmentation in the UK Retail Market

“…Our study K-means [28] 0.57 K-means [6] 0.61 DBSCAN [5] 0.72 Agglomerative [28] 0.57 K-means 0.64 BIRCH 0.64 DBSCAN 0.62 Agglomerative 0.64 Gaussian Mixture Model 0.80…”

“…It is suitable for urban-scale loading assessments and real-time online education. Hicham and Karim [5] proposed a clustering ensemble method which consists of DBSCAN, k-means, MiniBatch k-means, and the mean shift algorithm for customer segmentation. They applied their clustering ensemble method to 35,000 records and achieved a Silhouette Score of 0.72.…”

“…The density-based spatial clustering of applications with noise (DBSCAN) algorithm is a useful tool for identifying complex structures in consumer data for customer segmentation. It is particularly effective in recognising clusters of various shapes and densities, particularly in datasets with noise and irregularities [5]. DBSCAN groups data points based on their proximity and density, identifying core points with a minimum number of nearby points.…”

Customer sentiment analysis for Arabic social media using a novel ensemble machine learning approach

“…For learning and classification, machine learning algorithms use a variety of series [24]. The training set consists of both the classes and names of the feature vectors that are being input.…”

Enhancing Arabic E-Commerce Review Sentiment Analysis Using a hybrid Deep Learning Model and FastText word embedding

“…Both DL and ML have the potential to automate text analysis as well as the extraction of sentiment [4], [5]. The CNN and CNN-LSTM hybrid models have significantly improved sentiment analysis [6].…”