Data Mining : Concepts and Techniques 3rd edition Ed. 3

By Jiawei Han, Micheline Kamber, Jian Pei. 2011

1,165 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/0353e7f78ad385e9e898d9a9de18a2aa7ed9d9ed

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Citations in the corpus

A taxonomy of privacy-preserving record linkage techniques (2013, background): “…data collections through the use of data mining and analytics technologies can provide a competitive edge to a commercial enterprise, can allow improved crime and fraud detection, can lead to better patient outcomes in the health sector, and can be of vital importance to national security [1].”
Big Data, new epistemologies and paradigm shifts (2014, background): “These new techniques are rooted in research concerning artiﬁcial intelligence and expert systems that have sought to produce machine learning that can computationally and automatically mine and detect patterns and build predictive models and optimize outcomes (Han et al., 2011; Hastie et al., 2009).”
A survey on unsupervised outlier detection in high‐dimensional numerical data (2012, background): “[1–8], to name a few), the difﬁculties in high-dimensional data and specialized approaches in this area have not been sketched in any of those (though, notably, a recent textbook edition sketches three example approaches [9]).”
Rethinking Graph Neural Networks for Anomaly Detection (2022, methodology): “An anomaly or an outlier is a data object that deviates sig-niﬁcantly from the majority of the objects, as if it was generated by a different mechanism (Han et al., 2011).”
Big Data Creates New Opportunities for Materials Research: A Review on Methods and Applications of Machine Learning for Materials Design (2019, unknown): “Several algorithms [39] such as principal component analysis (PCA), multi-dimensional scaling (MDS), and linear discriminant analysis (LDA) are available to reduce the dimension of the feature space and help identify the most relevant descriptors (or key features) for ML.”