Streaming k-Means Clustering with Fast Queries
Data Structures and Algorithms
2018-12-10 v2 Software Engineering
Abstract
We present methods for k-means clustering on a stream with a focus on providing fast responses to clustering queries. Compared to the current state-of-the-art, our methods provide substantial improvement in the query time for cluster centers while retaining the desirable properties of provably small approximation error and low space usage. Our algorithms rely on a novel idea of "coreset caching" that systematically reuses coresets (summaries of data) computed for recent queries in answering the current clustering query. We present both theoretical analysis and detailed experiments demonstrating their correctness and efficiency
Cite
@article{arxiv.1701.03826,
title = {Streaming k-Means Clustering with Fast Queries},
author = {Yu Zhang and Kanat Tangwongsan and Srikanta Tirthapura},
journal= {arXiv preprint arXiv:1701.03826},
year = {2018}
}