Data clustering: an essential technique in data science

• URL: http://arxiv.org/abs/2412.18760v2
• Date: Thu, 30 Jan 2025 21:57:33 GMT
• Title: Data clustering: an essential technique in data science
• Authors: Tai Dinh, Wong Hauchi, Daniil Lisik, Michal Koren, Dat Tran, Philip S. Yu, Joaquín Torres-Sospedra,
• Abstract summary: The paper highlights key principles underpinning clustering, outlines widely used tools and frameworks, and introduces the workflow of clustering in data science. The paper concludes with insights into future research directions, emphasizing clustering's role in driving innovation and enabling data-driven decision-making.
• Score: 28.124442353352183
• License:
• Abstract: This paper explores the critical role of data clustering in data science, emphasizing its methodologies, tools, and diverse applications. Traditional techniques, such as partitional and hierarchical clustering, are analyzed alongside advanced approaches such as data stream, density-based, graph-based, and model-based clustering for handling complex structured datasets. The paper highlights key principles underpinning clustering, outlines widely used tools and frameworks, introduces the workflow of clustering in data science, discusses challenges in practical implementation, and examines various applications of clustering. By focusing on these foundations and applications, the discussion underscores clustering's transformative potential. The paper concludes with insights into future research directions, emphasizing clustering's role in driving innovation and enabling data-driven decision-making.

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