Comparison of Density-Based Spatial Clustering of Applications with Noise (DBSCAN), K-Means and X-Means Algorithms on Shopping Trends Data
Keywords:Davies-Bouldin Index, DBSCAN, K-Means, Shopping Data, X-Means
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.