Data Mining Practical Machine Learning Tools And Techniques With Java Implementations

By Marcel Abendroth. 2016

1,615 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/528ecb0f88a9ea6110ba309b98cc2f0678f257c9

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Citations in the corpus

Review and Comparison of Associative Classification Data Mining Approaches (2014, unknown): “task [3] is a common example of unsupervised learning where the aim is to discover the correlations among items in the shopping carts [1].”
AllenNLP: A Deep Semantic Natural Language Processing Platform (2018, methodology): “This emphasis on conﬁgur-ing parameters, training, and evaluating is similar to Weka (Witten and Frank, 1999) or Scikit-learn (Pedregosa et al., 2011), but AllenNLP focuses on cutting-edge research in deep learning and is designed around declarative conﬁguration of model architectures in addition…”
Phishing detection based Associative Classification data mining (2014, methodology): “Many research studies including (Jabbar et al., 2013) revealed that AC usually extracts better classifiers with reference to error rate than other classification approaches like decision tree, and rule induction (Witten and Frank, 2002).”
CatBoost for big data: an interdisciplinary review (2020, methodology): “The models used are as follows: from the Weka system, 1R rule learning [43], C4.5 Decision Tree [67], SVM [64] and Multiboost [84].”
Predicting human behavior in unrepeated, simultaneous-move games (2013, methodology): “However, in five other datasets, including All10, the situation was reversed, with Lk and Poisson-CH outperforming QRE and NI.”
Educational data mining: A survey and a data mining-based analysis of recent works (2014, methodology): “Data mining in a nutshell According to Witten and Frank (2000), DM is the process oriented to extract useful and comprehensible knowledge, previously unknown, from huge and heterogeneous data repositories.”
Comparison between M5 Model Tree and Neural Networks for Estimating Reference Evapotranspiration in an Arid Environment (2014, methodology): “For creating M5 model tree, based on training data set, the Weka software (Witten and Frank 2005) was used.”
Recent progress in biohydrometallurgy and microbial characterisation (2018, background): “Data mining is a statistical approach for generating new useful knowledge or information from sizeable multivariate data sets, which would otherwise be difficult to analyse manually (Witten and Frank, 2000).”
Transfer learning: a friendly introduction (2022, methodology): “To date, the disciplines of traditional ML and data mining have been extensively applied in many areas, such as retrieving patterns from existing records obtained from labeled or unlabeled data sets, for instance, training data, to predict future occurrences [5].”