The Top Ten Algorithms in Data Mining

By Xindong Wu, Vipin Kumar. 2009

721 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/85f57aa4b0bc3a443bbd076ed352106721f4a5cc

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Citations in the corpus

kNN-IS: An Iterative Spark-based design of the k-Nearest Neighbors classifier for big data (2017, background): “In [4] , the kNN was claimed to be one of the ten most influential data mining algorithms.”
Multi-instance multi-label learning (2008, neutral): “5, the Boosting process will stop [89].”
On the characterization of noise filters for self-training semi-supervised in nearest neighbor classification (2014, methodology): “We will adopt the Nearest Neighbor (NN) rule [36] as the base classifier, which has been highlighted as one of the most influential techniques in data mining [37].”
A naive Bayes model for robust remaining useful life prediction of lithium-ion battery (2014, methodology): “2 with the SVM [24,25], another popular data-driven method [21] known for its”
Web-scale k-means clustering (2010, methodology): “We tested the mini-batch k-means against both Lloyd’s batch k-means [6] and the SGD variant of [1].”
Prediction of Slope Stability using Naive Bayes Classifier (2018, background): “As there are only two classes in this study, it is common to use a threshold of 1/2 (Wu and Kumar, 2009).”
Big data preprocessing: methods and prospects (2016, neutral): “For example, three of the ten methods considered as the top ten in data mining [37] need an external or embedded discretization of data: C4.”
Self-labeled techniques for semi-supervised learning: taxonomy, software and empirical study (2015, background): “All of these selected base classifiers have been considered as one of the ten most influential data mining algorithms in [76].”