Data Mining And Knowledge Discovery Handbook

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec (2017, methodology): “Take the PPI dataset with 10% training data as an example, NetMF ( T = 1) achieves relatively 18.15% and 13.19% performance gains over LINE (2nd) in terms of Micro-F1 and Macro-F1 scores, respectively.”
Big Data in product lifecycle management (2015, methodology): “” Data Mining has been applied to uncover useful information and identify telecommunication fraud [49].”
ClustVis: a web tool for visualizing clustering of multivariate data using Principal Component Analysis and heatmap (2015, methodology): “The package uses popular clustering distances and methods (13) implemented in dist and hclust functions in R.”
Text Classification Algorithms: A Survey (2019, methodology): “Micro-averaged results [155,217] can be computed as follows:”
Comprehensibility of Classification Trees–Survey Design (2019, background): “However, comprehensibility is ill-defined [10] and subjective [1, 8, 13], therefore it is difficult to measure.”
Multi-target regression via input space expansion: treating targets as inputs (2012, background): “Despite their commonalities, MTR and MLC have typically been treated in isolation and only few works (Blockeel et al 1998; Weston et al 2002; Teh et al 2005; Balasubramanian and Lebanon 2012) have given a general formulation of their key ideas, recognizing the dual applicability of their approaches.”