2008/08/13 by Ping Li, Li, Ping · 2 citations
Computer Science · #Bayesian Methods and Mixture Models #Data Stream Mining Techniques #Data Structures and Algorithms (cs.DS) #FOS: Computer and information sciences #Machine Learning and Algorithms
paper · pdf · doi:10.48550/arxiv.0808.1766
openalex publication_date 2008/08/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Compressed Counting (CC) was recently proposed for very efficiently computing the (approximate) αth frequency moments of data streams, where 0 1, the complexity of CC (using the geometric mean estimator) is O(1/ε), breaking the well-known large-deviation bound O(1/ε2). The case α≈ 1 has important applications, for example, computing entropy of data streams. For practical purposes, this study proposes the optimal quantile estimator. Compared with previous estimators, this estimator is computationally more efficient and is also more accurate when α> 1.