arXiv:2407.11217v4 Announce Type: replace-cross Abstract: Clustering problems such as $k$-means and $k$-median are staples of unsupervised learning, and many algorithmic techniques have been developed to tackle their numerous aspects. In this paper, we focus on the class of greedy approximation algorithm, that attracted less attention than local-search or primal-dual counterparts. In particular, we study the recursive greedy algorithm developed by Mettu and Plaxton [SIAM J. Comp 2003]. We provide a simplification of the algorithm, allowing for faster implementation, in graph metrics or in Eucl
The continuous evolution of AI algorithms necessitates constant improvement in efficiency and simplicity to handle ever-growing datasets and computational demands.
Improved algorithms for fundamental machine learning tasks like k-means and k-median lead to faster model training and potentially more efficient AI systems across various applications.
This simplifies and speeds up a previously known greedy clustering algorithm, making it more practical for real-world large-scale data analysis.
- · AI researchers and developers
- · Data scientists
- · Cloud computing providers
- · Companies using unsupervised learning
- · Inefficient clustering algorithm implementations
Faster clustering algorithms can reduce computational costs and time for data preprocessing in machine learning pipelines.
The cost savings and increased efficiency could enable the application of clustering to larger datasets or in more time-sensitive scenarios.
This could accelerate scientific discovery and industrial innovation in fields that rely heavily on data analysis and pattern recognition.
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Read at arXiv cs.AI