Data Mining and Analysis: Fundamental Concepts and Algorithms

By Mohammed J. Zaki. 2014

996 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/f2b66923db74a16169d040a51ada555d5b6f8851

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Citations in the corpus

Models for early prediction of at-risk students in a course using standards-based grading (2016, methodology): “In this study, the accuracy of the predictions reached their best performance after the training dataset contained at least (Zaki & Meira, 2014).”
Big data preprocessing: methods and prospects (2016, methodology): “The set of techniques used prior to the application of a data mining method is named as data preprocessing for data mining [16] and it is known to be one of the most meaningful issues within the famous Knowledge Discovery from Data process [17, 18] as shown in Fig.”
Visualizing Big Data with augmented and virtual reality: challenges and research agenda (2015, methodology): “Those are the basis for data analysis techniques such as Data Mining [39, 46–49], Neural Networks [41, 50–52], Machine Learning [53–55], Signal Processing [56–58] and Visualization Methods [59–61].”
Processing Social Media Messages in Mass Emergency (2014, methodology): “For an overview of clustering methods, see [Zaki and Meira 2014, Part III].”
Interactive and Deterministic Data Cleaning (2016, background): “The concept of closed frequent itemset is widely used in data mining (see [48] for a survey), where it refers to a set of itemsets that are both frequent (i.”
Hyperparameter Optimization: Comparing Genetic Algorithm against Grid Search and Bayesian Optimization (2021, background): “…a good comparison among the different algorithms for the hyperparameters optimization process, the following criteria have been chosen to be included in the evaluation; more explanation can be found in [14]: 1) Accuracy: The accuracy of a classiﬁer is one metric for assessing classiﬁcation models.”