Summary of On Simplifying Large-scale Spatial Vectors: Fast, Memory-efficient, and Cost-predictable K-means, by Yushuai Ji et al.
On Simplifying Large-Scale Spatial Vectors: Fast, Memory-Efficient, and Cost-Predictable k-means
by Yushuai Ji, Zepeng Liu, Sheng Wang, Yuan Sun, Zhiyong Peng
First submitted to arxiv on: 3 Dec 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 proposes Dask-means, a fast, memory-efficient, and cost-predictable k-means algorithm designed for processing large-scale spatial vectors on resource-constrained devices. The algorithm accelerates k-means by using an optimized nearest neighbor search over a memory-tunable index to assign vectors to clusters in batches, reducing memory usage and computational time. A lightweight cost estimator predicts the memory cost and runtime of the task, allowing Dask-means to request appropriate memory or adjust its required space to meet constraints. Experiments show that Dask-means uses less than 30MB of memory when simplifying datasets with scales like 10^6, achieving over 168 times speedup compared to Lloyd’s algorithm. The paper also validates Dask-means on mobile devices, demonstrating significant speedup and low memory cost compared to state-of-the-art k-means algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it possible for devices with limited resources to quickly process large amounts of location data or 3D point clouds. The authors created a new algorithm called Dask-means that is fast, uses little memory, and can predict how much memory it will need. This helps devices like smartphones process big datasets quickly without running out of memory or taking too long. Experiments show that Dask-means works well on large datasets and is much faster than other similar algorithms. |
Keywords
» Artificial intelligence » K means » Nearest neighbor
