RapidMiner: Data Mining Use Cases and Business Analytics Applications

By M. Hofmann, Ralf Klinkenberg. 2013

517 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Science Applications

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Citations in the corpus

Mordred: a molecular descriptor calculator (2018, background): “It provides several interfaces (e.g., a graphical user interface (GUI), command line interface (CLI), Konstanz Information Miner (KNIME) [13], and Rapid Miner [14]), and it has been cited by other papers more than 300 times.”
AutoML to Date and Beyond: Challenges and Opportunities (2020, background): “…[21] Klein et al. [ 35] OpenML-CC18 [U63] Duan et al. [15] AutoML Challenges [U64] Olson et al. [48] Eggensperger et al. [16] (Continued) RapidMiner [24] Orange [13] For each subtask, we list related and notable research contributions, survey articles, open source systems, and commercial systems.”
Modelling Multi-Component Predictive Systems as Petri Nets (2017, methodology): “2016), RapidMiner (Hofmann and Klinkenberg 2016) or Knime (Berthold et al.”
Ensembles for feature selection: A review and future trends (2019, unknown): “RapidMiner [84] is a data science software platform that provides several feature selection tools, including information gain, Gini index, chi-square, and others.”
MacroBase: Analytic Monitoring for the Internet of Things (2016, methodology): “leverage detectors based on robust statistics [46,57], adapted to the streaming context. However, in this paper, we also demonstrate compatibility with detectors from Elki [72], Weka [38], RapidMiner [45], and OpenGamma [2]. Data explanation. Data explanation techniques assist in summarizing differe”
MacroBase: Prioritizing Attention in Fast Data (2016, methodology): “In Appendix D, we provide additional results examining the distribution of outlier scores, the effect of varying support and risk ratio, the effect of training over samples and operating over varying metric dimensions, the behavior of the M-CPS tree, preliminary scale-out behavior, comparing the run”
PyOD: A Python Toolbox for Scalable Outlier Detection (2019, methodology): “To help approach these problems, established outlier detection packages exist in various programming languages such as ELKI Data Mining (Achtert et al., 2010) and RapidMiner (Hofmann and Klinkenberg, 2013) in Java and outliers package (Komsta and Komsta, 2011) in R.”