Discovering Knowledge in Data: An Introduction to Data Mining

By Nitesh V. Chawla. 2005

2,708 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/f4156a05a47fdeda30638e10954d3674cc056ab6

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Citations in the corpus

TSclust: An R Package for Time Series Clustering (2014, methodology): “This clustering evaluation procedure is intuitive, straightforward to implement, parameter-free and asymptotically optimal in the Bayes sense (Tan, Steinbach, and Kumar 2006).”
A Physics-Constrained Data-Driven Approach Based on Locally Convex Reconstruction for Noisy Database (2019, background): “close connection with the various areas such as statistics, data mining, and machine learning, which allow the extraction of insightful information or the hidden structures from large volumes of data [4] for enhanced scientific computing. The data-driven approaches, such as machine learning [5,6], h”
A comparison of machine learning techniques for customer churn prediction (2015, methodology): “The dataset has been used in numerous publications [22, 23, 24, 25, 26, 27, 28].”
Impact of Dataset Size on Classification Performance: An Empirical Evaluation in the Medical Domain (2021, background): “In practice, however, collecting medical data faces many challenges due to patients’ privacy, lack of cases due to rare conditions [4], as well as organizational and legal challenges [5,6].”
On truth discovery in social sensing: A maximum likelihood estimation approach (2012, methodology): “We then generated an observation matrix from these tweets by clustering them based on the Jaccard distance metric (a simple but commonly used distance metric for micro-blog data [28]).”
Data Mining in Healthcare and Biomedicine: A Survey of the Literature (2012, background): “(2) SVM is basically designed to resolve two-class classification problems.”
Data Classification: Algorithms and Applications (2014, background): “The reader is advised to refer to [84] for the specific details of the solution to the optimization formulation.”
Visualizing Big Data with augmented and virtual reality: challenges and research agenda (2015, methodology): “The most advanced techniques to analyze large amounts of data include: artificial neural networks [37–39]; models based on the principle of the organization and functioning of biological neural networks [40, 41]; methods of predictive analysis [42]; statistics [43, 44]; Natural Language Processing [”
Decision Tree Analysis on J48 Algorithm for Data Mining (2013, methodology): “It is the use of software techniques for finding patterns and consistency in sets of data [12].”
Standardization and Its Effects on K-Means Clustering Algorithm (2013, methodology): “Therefore, one should not employ distance measures like the Euclidean distance without having normalization of the data sets (Aksoy and Haralick, 2001; Larose, 2005).”
The CART decision tree for mining data streams (2014, methodology): “Among the plenty of techniques and methods used in machine learning or data mining, the classification seems to be one of the most important [14] [21] [31] [35].”