Data mining with big data

By Xindong Wu, Xingquan Zhu, Gong-Qing Wu, Wei Ding. IEEE Transactions on Knowledge and Data Engineering, 2016

1,905 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/391a5f286f814d852dddcab1b2b68e5c1af6c79e

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In Management Science and Operations Research

Citations in the corpus

Deep Computation Model for Unsupervised Feature Learning on Big Data (2016, methodology): “To evaluate the performance of the proposed model, we carried out the experiments on four representative datasets by comparison with stacking auto-encoders and multimodal deep learning models.”
Learning k for kNN Classification (2017, background): “Classiﬁcation is one of most important research topics in data mining (especially for big data mining) [Sun and Reddy 2013; Wu et al. 2014, 2015; Zhu et al. 2016b; Li et al. 2015].”
Data Mining for the Internet of Things: Literature Review and Challenges (2015, background): “Researchers have expanded existing data mining methods in many ways, including the efficiency improvement of single-source knowledge discovery methods, designing a data mining mechanism from a multisource perspective, and the study of dynamic data mining methods and the analysis of stream data [149]”
Industrial Internet: A Survey on the Enabling Technologies, Applications, and Challenges (2017, background): “, big data [3], cloud computing [4], networking [5], 3D printing [6], artificial intelligence [7], etc.”
A Survey on Information and Communication Technologies for Industry 4.0: State-of-the-Art, Taxonomies, Perspectives, and Challenges (2019, background): “Regarding other enablers, research has also acknowledged the driving force of FLOSS, for the IoT specifically [178], for Robotics [179], Additive Manufacturing [180], Cloud Computing [111], and Big Data [182].”
Remote Sensing Image Classification: No Features, No Clustering (2015, background): “5 quintillion bytes of data are created and 90% of the data in the world today were produced within the past 2 years [1].”
Machine Learning With Big Data: Challenges and Approaches (2017, background): “[23], categorized the challenges, but their categorization is according to three tiers: Tier I (Big Data mining platforms), Tier II (Semantics and application knowledge), and Tier III (Big Data mining algorithms).”
A Comprehensive Survey on Automatic Knowledge Graph Construction (2023, methodology): “[28] summarize the challenges facing knowledge discovery in big data environments with the HACE theorem, which is shown in Fig.”
CRISP-DM Twenty Years Later: From Data Mining Processes to Data Science Trajectories (2021, methodology): “• HACE [42], a Big Data processing framework based on a three tier structure: a “Big Data mining platform” (Tier I), challenges on information sharing and privacy, and Big Data application domains (Tier II), and Big Data mining algorithms (Tier III).”
Industrial Big Data in an Industry 4.0 Environment: Challenges, Schemes, and Applications for Predictive Maintenance (2017, background): “Tremendous amounts of industrial data are increasingly generated by manufacturing companies [2].”
A Parallel Random Forest Algorithm for Big Data in a Spark Cloud Computing Environment (2017, background): “The scale of global data continuously increases at a rate of 2 times every two years [1].”
History and trends in solar irradiance and PV power forecasting: A preliminary assessment and review using text mining (2018, methodology): “PCA Principal Component Analysis An orthogonal transformation that converts a set of observations to a set of linearly uncorrelated variables Jolliffe (1986); Yang et al. (2017a) Principal Components Analysis (I) PDF Probability Density Function Describes the distribution of continuous random variab”