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- W4382052504 abstract "The digital world generates massive volumes of data, and it is critical to analyse them. With machine learning and powerful visualization tools, the competition to innovate and succeed in this industry has made a significant leap towards tailoring data-driven solutions. Analyzing these data has never been easier and the insights gathered can transform many businesses. In this paper, four different clustering techniques were compared namely, K-means, Agglomerative, Birch, and Spectral clustering. Three separate performance metrics were used to find the effectiveness of these algorithms: the Silhouette coefficient, the Calinski-Harabasz score, and the Davies-Bouldin index. Based on the analysis, it was found that the K-means clustering algorithm outscored the other three by a higher performance measure score. The insights gathered from clustering led to the formulation of critical business strategies. This study also provides an in-depth look into expressing corporate strategies with advanced machine learning technologies. There are several applications to it, including classification, clustering, prediction, and big data visualization." @default.
- W4382052504 created "2023-06-27" @default.
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- W4382052504 creator A5092266443 @default.
- W4382052504 date "2023-05-05" @default.
- W4382052504 modified "2023-09-25" @default.
- W4382052504 title "Propensity modelling for predicting customer behaviour using clustering techniques for effective business analysis" @default.
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- W4382052504 doi "https://doi.org/10.1109/vitecon58111.2023.10157623" @default.
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