Summary of Categorical Data Clustering: 25 Years Beyond K-modes, by Tai Dinh et al.
Categorical data clustering: 25 years beyond K-modes
by Tai Dinh, Wong Hauchi, Philippe Fournier-Viger, Daniil Lisik, Minh-Quyet Ha, Hieu-Chi Dam, Van-Nam Huynh
First submitted to arxiv on: 30 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a comprehensive review of categorical data clustering methods over the past 25 years, building upon the introduction of K-modes. The authors highlight the importance of categorical data clustering across various fields such as health sciences, natural sciences, social sciences, education, engineering, and economics. The review compares practical implementations of algorithms, showcasing distinct methodologies and evaluating performance on benchmark datasets. Furthermore, the paper discusses challenges and opportunities in the field. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Categorical data is a type of data that can’t be measured or compared like numbers. This makes it harder to organize and analyze. Researchers have developed special ways to group this kind of data together, which is important for many applications. The paper looks back at what’s been done over the past 25 years in this area. It shows how these methods are used in different fields like healthcare, science, social sciences, education, engineering, and economics. The review also compares different algorithms and their performance on various datasets. |
Keywords
» Artificial intelligence » Clustering
