Summary of On the Use Of Relative Validity Indices For Comparing Clustering Approaches, by Luke W. Yerbury et al.
On the Use of Relative Validity Indices for Comparing Clustering Approaches
by Luke W. Yerbury, Ricardo J. G. B. Campello, G. C. Livingston Jr, Mark Goldsworthy, Lachlan O’Neil
First submitted to arxiv on: 16 Apr 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 investigates the suitability of Relative Validity Indices (RVIs) in selecting a Similarity Paradigm (SP) for clustering. Traditional RVIs are used to evaluate and optimize clustering outcomes, but this study explores their use in guiding the choice of SP. The authors conducted extensive experiments with seven popular RVIs on over 2.7 million clustering partitions of synthetic and real-world datasets, revealing fundamental conceptual limitations that undermine the use of RVIs for SP selection. Instead, practitioners are recommended to select SPs using external validation or outcome-oriented objective criteria, considering dataset characteristics and domain requirements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how good different methods are at choosing a way to cluster data. Right now, people often use Relative Validity Indices (RVIs) to help decide which method is best. But this paper asks if RVIs are really the right tool for the job. The authors tested seven different RVIs on lots of different datasets and found that they’re not very good at helping with clustering choices. Instead, they suggest using special kinds of validation or looking at what kind of data you have to choose the best method. |
Keywords
» Artificial intelligence » Clustering
