Data Mining: Concepts and Techniques

By Jiawei Han, Micheline Kamber. 2000

13,888 citations recorded by the source corpus.

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Citations in the corpus

A survey of data fusion in smart city applications (2019, methodology): “sensors collecting data with better coverage and power efﬁciency of the communication platform. With the large amounts of data becoming readily available in a smart city, data mining techniques [37], [38] are commonly used in the collected data. It helps in identifying the essential and important da”
Efficient machine learning models for prediction of concrete strengths (2021, methodology): “GBMs or gradient tree boosting originally proposed by Friedman [28] is a boosting ma183 chine learning model which utilises a sequences of “weak” or “base” learners with the aim of 184 creating an arbitrarily accurate “strong” learner [28, 29, 39, 40].”
Data mining combined to the multicriteria decision analysis for the improvement of road safety: case of France (2019, background): “• Algorithms for mining closed itemset: Close [38], Pascal [39].”
Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series (2018, methodology): “ecide to use a min-max normalization per type of feature. The traditional min-max normalization performs a subtraction of the minimum, then a division by the range, i.e. the maximum minus the minimum [66]. As this normalization is highly sensitive to extreme values, we propose to use 2 % (or 98 %) p”
Intrusion detection systems for IoT-based smart environments: a survey (2018, background): “2- Low false positive rate • A data mining approach is a means for extracting knowledge from a large amount of data, analogous to extracting gold from numerous rocks and sand [65].”
Guns, swords and data: Clustering of player behavior in computer games in the wild (2012, background): “K-means is perhaps the most widely adopted unsupervised clustering algorithm, and is theoretically suited for game telemetry features, however, it is focused on retrieving compact cluster regions, and can therefore in practice be hard to interpret [35].”
Survey on supervised machine learning techniques for automatic text classification (2019, background): “The supervised machine learning that underlying input–output comparative is learned utilizing little number of learning data and then output values for unknown input texts have been forecasted (Han et al. 2011).”