Mathematical Programming for Data Mining: Formulations and Challenges

By Paul S. Bradley, Usama M. Fayyad, Olvi L. Mangasarian. 1999

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Citations in the corpus

Supersparse linear integer models for optimized medical scoring systems (2015, methodology): “We then set C0 = 0.9W −/NP so that the optimization would yield a classifier that did not sacrifice accuracy for sparsity (see Remark 3).”
Supersparse Linear Integer Models for Interpretable Classification (2013, background): “Nevertheless, most feature selection relies on greedy optimization and cannot guarantee an optimal balance between accuracy and sparsity (with some exceptions, see e.g. [9]).”
Robust support vector method for hyperspectral data classification and knowledge discovery (2004, background): “In such an approach, the last objective is “to process the data in order to extract valid, novel, potentially useful, and ultimately understandable structure in data” [22] (see also citeseer.”
Multi-group support vector machines with measurement costs: A biobjective approach (2008, unknown): “[2, 5, 8, 9, 23, 28]), such as Supervised Classification.”
Dynamic pricing in the presence of inventory considerations: research overview, current practices, and future directions (2003, background): “Some of these data mining problems, e.g., clustering problems, can be modeled as large-scale mathematical programs and have been studied by OR/MS researchers for quite some time [9] [48].”
An introduction to kernel-based learning algorithms (2001, background): “In [83], a slightly more general approach for data mining problems is considered.”
Mining market data: A network approach (2006, background): “The essence of clusteringis partitioning the elements in a certain dataset into several distinct subsets (clusters) grouped according to an appropriate similarity criterion [20].”
Primal-dual approximation algorithms for metric facility location and k-median problems (1999, background): “In recent years, ths problem has found new clustering applications in the area of data mining (see [7]).”
Oblique Multicategory Decision Trees Using Nonlinear Programming (2005, background): “Section 5 concludes the paper and discusses future directions for this research.”
Supervised classification and mathematical optimization (2013, background): “Mathematical Optimization has played a crucial role in Supervised Classification [21] [22] [31] [32] [84] [81] [88] [107] [218] [242].”
A coordinate gradient descent method for nonsmooth separable minimization (2008, methodology): “[5,7,10,11,34,50,51].”
Statistical analysis of financial networks (2005, unknown): “Partitioning a dataset into sets (clusters) of elements grouped according to a certain criterion is referred to as clustering, which is one of the well-known data mining problems (Bradley et al., 1999).”
Approximation Algorithms for the Class Cover Problem (2004, background): “Examples of problems that have been recently studied include Schulman's new work [11] and others (see, for example, Bradley, Fayyad, and Mangasarian's survey [2]).”
White Rose Consortium ePrints Repository (unknown): “It is also data-order independent unlike standard k-means where the order of the input data aﬀects the positioning of the cluster centres and Bradley (Bradley et al., 1999) shows that k-medoids provides better class separation than k-means and hence is better suited to a novelty recognition task due”