Enhancing Beer Recommendations through Clustering: A Comparison of Hierarchical and K-means Clustering Methods on Normalized Data

Zhu, Tiansheng and Han, Yina (2023) Enhancing Beer Recommendations through Clustering: A Comparison of Hierarchical and K-means Clustering Methods on Normalized Data. In: Proceedings of the 2nd International Conference on Mathematical Statistics and Economic Analysis, MSEA 2023, May 26–28, 2023, Nanjing, China.

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Abstract

The process of normalization confers notable advantages in optimizing gradient descent algorithms for machine learning applications and simplifying data processing. Through normalization, the scale of numeric types in the dataset can be adjusted, thereby facilitating the identification of patterns

Item Type: Conference or Workshop Item (UNSPECIFIED)
Date Deposited: 04 Mar 2026 16:32
Last Modified: 17 Apr 2026 02:36
URI: http://eprints.eai.eu/id/eprint/44216

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