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2021 IEEE International Conference on Information Technologies

Analyzing Performance of Clustering Algorithms on a Real Retail Dataset

Ledion Lico
Indrit Enesi
Betim Cico
Aba Business Centre
Department of Electronics and Telecommunications – Polytechnic University of Tirana
Department of Computer Science – Metropolitan University of Tirana
Albania
Abstract:

Customer Relationship Management technology groups customers based on their transaction details.  The focus of the paper is finding groups of clients with similar buying patterns in a department store using these data for targeted marketing. The performance of K-means, K-Medoids, Agglomerative Clustering and DBSCAN is compared on a real retail dataset of a department store. Results show that the K-Medoids algorithm is more robust in case of noise and outliers in data. K-Means and DBSCAN perform better in terms of time, especially for large retails datasets.

Key words:
Agglomerative
DBSCAN
cluster techniques
K-means
K-Medoids