Summary of Neurcam: Interpretable Neural Clustering Via Additive Models, by Nakul Upadhya and Eldan Cohen
NeurCAM: Interpretable Neural Clustering via Additive Models
by Nakul Upadhya, Eldan Cohen
First submitted to arxiv on: 23 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 This paper introduces the Neural Clustering Additive Model (NeurCAM), a novel approach to interpretable clustering that leverages neural generalized additive models. Unlike traditional decision trees, NeurCAM provides fuzzy cluster membership with additive explanations of obtained clusters. To promote sparsity in model explanations, selection gates are introduced to limit feature and pairwise interactions used. The authors demonstrate text clustering capabilities, considering contextual representations while providing explanations based on uni- or bi-word terms. Extensive experiments show that NeurCAM achieves comparable performance to black-box methods on tabular datasets while remaining interpretable. In fact, it outperforms other interpretable clustering approaches when clustering text data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes computers better at grouping similar things together (called clusters) and explaining why they grouped them that way. Most computers do this using decision trees, but those can get too complicated to understand. The new approach, called NeurCAM, uses a different method to group things and explain the results. It’s like a special kind of map that shows how similar things are related. This helps us understand patterns in data better. The researchers tested it on lots of different kinds of data and found that it works well, even when dealing with complex text data. |
Keywords
* Artificial intelligence * Clustering
