Make Big Data More Manageable with Smart Sampling

Table of Links

Abstract and 1 Introduction

2 Preliminaries and Related Work

2.1 On Sampling Strategies

2.2 Other Coreset Strategies

2.3 Coresets for Database Applications

2.4 Quadtree Embeddings

3 Fast-Coresets

4 Reducing the Impact of the Spread

4.1 Computing a crude upper-bound

4.2 From Approximate Solution to Reduced Spread

5 Fast Compression in Practice

5.1 Goal and Scope of the Empirical Analysis

5.2 Experimental Setup

5.3 Evaluating Sampling Strategies

5.4 Streaming Setting and 5.5 Takeaways

6 Conclusion

7 Acknowledgements

8 Proofs, Pseudo-Code, and Extensions and 8.1 Proof of Corollary 3.2

8.2 Reduction of k-means to k-median

8.3 Estimation of the Optimal Cost in a Tree

8.4 Extensions to Algorithm 1

References

6 Conclusion

In this work, we discussed the theoretical and practical limits of compression algorithms for center-based clustering. We proposed the first nearly-linear time coreset algorithm for k-median and k-means. Moreover, the algorithm can be parameterized to achieve an asymptotically optimal coreset size. Subsequently, we conducted a thorough experimental analysis comparing this algorithm with fast sampling heuristics. In doing so, we find that although the Fast-Coreset algorithm achieves the best compression guarantees among its competitors, naive uniform sampling is already a sufficient compression for downstream clustering tasks in well-behaved datasets. Furthermore, we find that intermediate heuristics interpolating between uniform sampling and coresets play an important role in balancing efficiency and accuracy.

Although this closes the door on the highly-studied problem of optimally small and fast coresets for k-median and k-means, open questions of wider scope still remain. For example, when does sensitivity sampling guarantee accurate compression with optimal space in linear time and can these conditions be formalized? Furthermore, sensitivity sampling is incompatible with paradigms such as fair-clustering [8, 15, 21, 43, 56] and it is unclear whether one can expect that a linear-time method can optimally compress a dataset while adhering to the fairness constraints.

7 Acknowledgements

Andrew Draganov and Chris Schwiegelshohn are partially supported by the Independent Research Fund Denmark (DFF) under a Sapere Aude Research Leader grant No 1051-00106B. David Sauplic has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 101034413.