Summary of A Scalable K-medoids Clustering Via Whale Optimization Algorithm, by Huang Chenan and Narumasa Tsutsumida
A Scalable k-Medoids Clustering via Whale Optimization Algorithm
by Huang Chenan, Narumasa Tsutsumida
First submitted to arxiv on: 30 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Performance (cs.PF)
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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 In this paper, researchers address the scalability issue of traditional unsupervised clustering methods like Partitioning Around Medoids (PAM) by introducing a novel approach called WOA-kMedoids. This method utilizes the Whale Optimization Algorithm (WOA), inspired by humpback whales’ hunting strategies, to optimize centroid selection and reduce computational complexity from quadratic to near-linear. This improvement enables WOA-kMedoids to efficiently cluster large datasets while maintaining high accuracy. The paper evaluates WOA-kMedoids on 25 time series datasets from the UCR archive and finds that it maintains clustering accuracy similar to PAM. While WOA-kMedoids has slightly higher runtime than PAM for small datasets, it outperforms PAM in larger datasets. This scalable and accurate approach makes WOA-kMedoids a promising choice for unsupervised clustering in big data applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Unsupervised clustering helps us find patterns in huge amounts of data without any labels. Traditional methods are good but slow when dealing with very large datasets. Scientists have created a new way to cluster called WOA-kMedoids, which is faster and just as accurate. They tested it on many different types of data and found that it works well even with big datasets. This means we can use it to discover useful information in massive amounts of data across various areas. |
Keywords
» Artificial intelligence » Clustering » Optimization » Time series » Unsupervised
