DATA MINING FOR BIG DATA

By Xindong Wu, Xingquan Zhu, Gongqing Wu, W. Ding. 2015

1,026 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/b016670e4e0986a85cc70a625e8627a80217a0a7

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Citations in the corpus

Privacy-Preserving Stochastic Gradual Learning (2018, background): “ that, PRESTIGE achieves a good tradeo between privacy preservation and robustness over baselines. 1 Introduction Learning from large-scale sensitive data stems from 2004 [1], and still keeps vibrant [2,3]. However, the direct use of learning algorithms on such data will lead to the issues of \compu”
Big data privacy: a technological perspective and review (2016, background): “Conclusion and future work Big data [2, 68] is analysed for bits of knowledge that leads to better decisions and strategic moves for overpowering businesses.”
Unlocking the circular economy through new business models based on large-scale data: An integrative framework and research agenda (2017, background): “This integration becomes more relevant as managing large amounts of data is a key organisational success factor; especially with the evolution of the ‘internet-of-things’, cyber and smart industry integration (Tien, 2013; Wang and Song, 2014; Watson et al., 2010; Wu et al., 2014).”
#Trending topics in careers: a review and future research agenda (2017, methodology): “We would propose that career researchers might use recent technological and methodological innovations such as social network analysis (Scott, 2017) and text mining (Kobayashi et al., 2017a, b; Wu et al., 2014) to complement this existing knowledge.”
A review of clustering techniques and developments (2017, background): “Big data [161-168] is also an emerging issue.”