The paper delves into the space complexity of Euclidean (k, z)-Clustering, offering insights on compression methods and dimension reduction. It establishes tight space bounds and highlights the importance of coresets in data compression. The study emphasizes the interplay between storage requirements and clustering efficiency.
Previous research has focused on data compression through coresets and dimension reduction techniques like Johnson-Lindenstrauss (JL) and terminal embedding. The paper introduces a novel approach to analyze the space complexity of clustering problems, shedding light on optimal compression schemes. By leveraging geometric insights and discrepancy methods, the study uncovers fundamental factors influencing the cost function's complexity.
The analysis showcases how large datasets impact storage requirements for clustering algorithms, emphasizing the significance of efficient compression methods. The study provides valuable insights into optimizing storage space while maintaining clustering accuracy in high-dimensional spaces.
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