Principles of Data Mining

By David J. Hand, Heikki Mannila, Padhraic Smyth. 2001

4,367 citations recorded by the source corpus.

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In Information Systems

Citations in the corpus

Interpreting random forest classification models using a feature contribution method (2013, methodology): “Clustering is an approach for grouping elements/objects according to their similarity [10].”
Strongly agree or strongly disagree?: Rating features in Support Vector Machines (2016, methodology): “It is natural to consider the problem of simultaneous optimization of both accuracy and interpretability, see for instance Hooker and Williams (2012), Müssel et al. (2012). This biobjective model deserves further analysis and testing.”
Simlarity Search for Web Services (2004, background): “Traditionally, cohesion is defined as the sum of squares of Euclidean distances from each point to the center of the cluster it belongs to; correlation is defined as the sum of squares of distances between cluster centers [14].”
A data mining approach to predict forest fires using meteorological data (2007, methodology): “Hence, the alternative is to use automated DM tools to analyze the raw data and extract high-level information for the decision-maker [10].”
Supervised classification and mathematical optimization (2013, neutral): “One of the most important tasks in Data Mining, [116] [121] [240], is Supervised Classification, which seeks procedures for classifying objects in a set Ω into a set C of classes.”
Codon adaptation index as a measure of dominating codon bias (2003, methodology): “The set of points can be visualized in three dimensions by using principal components analysis (PCA) (Hotelling, 1933; Hand et al ., 2001): ﬁrst, every coordinate is normalized on unity standard deviation to take into account equally dominating as well as rare codons [following the standard…”
A Survey of Classification Methods in Data Streams (2007, background): “Classification techniques [19, 201 have attracted the attention of researchers due to the significance of their applications.”
A survey on Data Mining approaches for Healthcare (2013, background): “Various studies highlighted that Data Mining techniques help the data holder to analyze and discover unsuspected relationship among their data which in turn helpful for making decision [3].”
Process Mining: Overview and Opportunities (2012, methodology): “Whereas classical data mining techniques are mostly data-centric [Hand et al. 2001], process mining is process-centric.”
Clustering Categories in Support Vector Machines (2017, background): “1 Introduction In Supervised Classi cation, [1, 14, 28], we are given a set of objects Ω partitioned, in its simplest setting, into two classes, and the aim is to classify new objects.”
Statistical strategies for avoiding false discoveries in metabolomics and related experiments (2007, background): “…1998; Michalski et al., 1998; Michalewicz and Fogel, 2000)) and data mining (e.g. (Adriaans and Zantinge, 1996; Cabena et al., 1998; Weiss and Indurkhya, 1998; Berry and Linoff, 2000; Hand et al., 2001; Rud, 2001)), but provide references for those whose wish to learn more about these methods.”
Clustering aggregation (2005, background): “For example, clustering categorical data is an instance of the clustering aggregation problem; each categorical attribute can be viewed as a clustering of the input rows where rows are grouped together if they take the same value on that attribute.”