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All Journal Indonesian Journal of Applied Technology and Innovation Science
Zhevin Alfian
Universitas Islam Negeri Sultan Syarif Kasim Riau, Indonesia
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Comparison of Density-Based Spatial Clustering of Applications with Noise (DBSCAN), K-Means and X-Means Algorithms on Shopping Trends Data Vina Wulandari; Yulia Syarif; Zhevin Alfian; Muhammad Adil Althof; Maylina Mufidah
IJATIS: Indonesian Journal of Applied Technology and Innovation Science Vol. 1 No. 1 (2024): IJATIS February 2024
Publisher : Institut Riset dan Publikasi Indonesia (IRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/ijatis.v1i1.1135

Abstract

This study extensively compares the efficacy of three clustering algorithms of DBSCAN, K-Means, and X-Means in analyzing shopping trend data, utilizing the Davies-Bouldin Index (DBI) for group validity assessment. The dataset, sourced from Kaggle.com, encompasses various customer attributes. Results indicate that the DBSCAN algorithm demonstrates superior cluster validity, outperforming K-Means and X-Means. Specifically, with an Eps value of 0.3 and MinPts value of 3, DBSCAN achieves an optimal DBI value of 0.1973. K-Means follows with a DBI value of 2.2958, and X-Means attains its best value (2.5663) with k=3. This research underscores the pivotal role of clustering algorithms in understanding shopping trends and customer preferences, offering valuable insights into their comparative performance.
Co-Authors Maylina Mufidah Muhammad Adil Althof Vina Wulandari Yulia Syarif