Data clustering: an essential technique in data science

This paper provides a comprehensive exploration of data clustering, emphasizing its methodologies and applications across different fields. Traditional techniques, including partitional and hierarchical clustering, are discussed alongside other approaches such as data stream, subspace and network clustering, highlighting their role in addressing complex, high-dimensional datasets. The paper also reviews the foundational principles of clustering, introduces common tools and methods, and examines its diverse applications in data science. Finally, the discussion concludes with insights into future directions, underscoring the centrality of clustering in driving innovation and enabling data-driven decision making.

Categorical data clustering: 25 years beyond K-modes

The clustering of categorical data is a common and important task in computer science, offering profound implications across a spectrum of applications. Unlike purely numerical datasets, categorical data often lack inherent ordering as in nominal data, or have varying levels of order as in ordinal data, thus requiring specialized methodologies for efficient organization and analysis. This review provides a comprehensive synthesis of categorical data clustering in the past twenty-five years, starting from the introduction of K-modes. It elucidates the pivotal role of categorical data clustering in diverse fields such as health sciences, natural sciences, social sciences, education, engineering and economics. Practical comparisons are conducted for algorithms having public implementations, highlighting distinguishing clustering methodologies and revealing the performance of recent algorithms on several benchmark categorical datasets. Finally, challenges and opportunities in the field are discussed.