Scalable Twitter user clustering approach boosted by Personalized PageRank

Naik, Anup; Maeda, Hideyuki; Kanojia, Vibhor; Fujita, Sumio

doi:10.1007/s41060-017-0089-3

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Scalability and Parallelism In Data Mining1

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2018

2024

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Cited by 4 publications

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“…• Parallel edge-based visual assessment of cluster tendency on GPU [2] In this paper, the authors improved the visual assessment of (cluster) tendency (VAT) algorithm such that it becomes suitable to GPU parallel processing. • Scalable Twitter user clustering approach boosted by Personalized PageRank [3] In this paper, the authors proposed a scalable approach for Twitter user clustering based on content and graph features with topical relevance and influence ranking by Personalized PageRank.…”

Section: Scalability and Parallelism In Data Miningmentioning

confidence: 99%

Introduction to the special issue on Data Science in Asia (with PAKDD’2017)

Cao¹,

Lee²,

Lin³

2018

Int J Data Sci Anal

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we accepted 45 papers as long-presentation papers. After the conference, we invited the authors of the long-presentation papers to submit an extended version of their papers to this special issue with JDSA. The reviewers of JDSA conducted thorough reviews for the extended papers, and the authors revised their papers based on the reviews. Finally, we accepted seven papers for this special issue.These seven papers are roughly classified into four categories, as below.

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Section: Scalability and Parallelism In Data Miningmentioning

confidence: 99%