A Farthest First Traversal based Sampling Algorithm for k-clustering

Trang, Le Hong; Hoang, Nguyen Le; Dang, Tran Khanh

doi:10.1109/imcom48794.2020.9001738

Cited by 9 publications

(9 citation statements)

References 19 publications

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“…The idea is based on ProTraS with the post-processing to improve the correctness [31]. -FFT-based with pre-processing improvement in Algorithm 3 [32].…”

Section: Comparative Resultsmentioning

confidence: 99%

“…After these proposal methods, there are plenty of improvements for coreset constructions that are also based on FFT. Two state-of-the-art methods are proposed in [31] and in [32]. We use these two for comparisons in next chapter.…”

Section: Farthest-first-traversal (Fft)-based Algorithmmentioning

confidence: 99%

“…ProTraS provides a good method to build coresets, but it still has some drawbacks. To overcome these problems, authors in [32] use a native FFT with pre-processing strategies to create a coreset. The first one is to find a specific initial point instead of randomly and the second one is a technique to reduce the computational complexity to make the construction much more faster.…”

Section: Fft-based With Pre-processing Improvementmentioning

confidence: 99%

“…Since these five algorithms need different input parameters, ProTraS and algorithm 2 need the value of while the others need sample size as input. Therefore, we first run ProTraS with post-processing in algorithm 2 for = 0.1 and = 0.2 , then we use the sample size from results of this first step and used as the input parameter for the Uniform Sampling, FFTbased with pre-processing improvement in Algorithm 3 [32], Adaptive Sampling in Algorithm 4 [14] and Lightweight Coreset in Algorithm 5 [7].…”

Section: Experiments Setupmentioning

confidence: 99%

“…In this paper, we use the version with improvement proposed in [31]. -The second coreset method is an idea based on native Farthest-First-Traversal algorithm in [32]. -The third coreset construction is Adaptive Sampling by Feldman et al [14].…”

Section: Introductionmentioning

confidence: 99%

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A Comparative Study of the Some Methods Used in Constructing Coresets for Clustering Large Datasets

2020

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Coresets can be described as a compact subset such that models trained on coresets will also provide a good fit with models trained on full data set. Using coresets, we can scale down a big data to a tiny one to reduce the computational cost of a machine learning problem. In recent years, data scientists have investigated various methods to create coresets with many techniques and approaches, especially for solving the problem of clustering large datasets. In this paper, we make comparisons among four state-of-the-art algorithms: ProTraS by Ros and Guillaume with improvements, Lightweight Coreset by Bachem et al. Adaptive Sampling Coreset by Feldman et al. and a native Farthest-First-Traversal-based coreset construction. We briefly introduce these four algorithms and compare their performances to find out the benefits and drawbacks of each one. Keywords Machine learning • Big data • Coreset • Clustering • Farthest-first-traversal • Sampling This article is part of the topical collection "Future Data and Security Engineering 2019" guest edited by Tran Khanh Dang.

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“…The idea is based on ProTraS with the post-processing to improve the correctness [31]. -FFT-based with pre-processing improvement in Algorithm 3 [32].…”

Section: Comparative Resultsmentioning

confidence: 99%

Section: Farthest-first-traversal (Fft)-based Algorithmmentioning

confidence: 99%

Section: Fft-based With Pre-processing Improvementmentioning

confidence: 99%

Section: Experiments Setupmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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