Data Mining: Concepts and Techniques, Second Edition

By Jiawei Han, Micheline Kamber. The Morgan Kaufmann series in data management systems, 2006

1,549 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/615cdebfef5e8cd1e0e8eef126e0fe7148fbf6a3

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Citations in the corpus

A survey of intrusion detection techniques in Cloud (2013, unknown): “The goal of using ANNs [35] for intrusion detection is to be able to generalize data from incomplete data and to be able to classify data as being normal or intrusive [36].”
A Fuzzy Association Rule-Based Classification Model for High-Dimensional Problems With Genetic Rule Selection and Lateral Tuning (2011, background): “Any classiﬁcation problem consists of N training patterns, i.e., x p = ( x p 1 , . . . , x pm ) , p = 1 , 2 , . . . , N , from S classes, where x pi is the i th attribute value ( i = 1 , 2 , . . . , m ) of the p th training pattern.”
Clustering algorithms: A comparative approach (2016, methodology): “This method is eﬃcient for large amounts of data because it does not explore the whole neighborhood of the data points [68], although the quality of the results have been found to strongly depend on the number of objects in the sample data [62].”
Sentiment analysis using product review data (2015, methodology): “For the detailed descriptions of CART, please see [32].”
Clustering to Find Exemplar Terms for Keyphrase Extraction (2009, background): “Clustering is an important unsupervised learning problem, which is the assignment of objects into groups so that objects from the same cluster are more similar to each other than objects from different clusters (Han and Kamber, 2005).”
Ontology-based information extraction: An introduction and a survey of current approaches (2010, unknown): “Using {Relevant} and {Retrieved} to denote the sets of relevant and retrieved documents respectively, precision and recall are often represented using the following formulae [66, 67].”
A survey of frequent subgraph mining algorithms (2012, background): “The pattern growth-based adopts a DFS strategy is depicted where, for each discovered subgraph g , the subgraph is extended recursively until all frequent supergraphs of g are discovered (Han & Kamber 2006).”
Anomaly detection in streaming environmental sensor data: A data-driven modeling approach (2010, methodology): “The PI requires an estimate of the standard deviation of the modeling error, for which 10-fold cross-validation (Duda et al., 2001; Han and Kamber, 2006) is used because it has been shown to quantify error caused by uncertainty in the data measurement and in the model parameter estimation (Han and…”
Predicting defect-prone software modules using support vector machines (2008, methodology): “For more details on these techniques see (Han and Kamber, 2001; Hosmer and Lemeshow, 2000; Duda et al., 2001; Webb, 2002; Breiman, 2001).”