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Radcliffe S.,SAS
Journal of the Institute of Telecommunications Professionals | Year: 2010

A Customer Value Optimization (CVO) approach drives a continuously improving process of learning by doing. It will help service providers to better understand how customers are engaging with the company, identify ways to reduce churn, and systematically record and analyze customer management actions to enhance best practices. The human resources department has to manage the organization chaos of mergers and acquisitions, along with staffing, training and development in support of new initiatives. CRM systems tend to emphasize data collection, standard reports (such as sales pipeline), and point-in-time indicators (such as last purchase date and amount). Traditional operational and transactional systems have not done much to help providers assess and optimize customer value, because these systems cannot assemble a unified view of the customer, and they lack the analytical rigor required to predict future outcomes and test strategy options. Source


Coyle C.L.,SAS | Peterson M.,University of Massachusetts Lowell
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2016

As part of a small team of user experience designers at an enterprise software company, we created designs for a full-featured business application, and included usability testing in our design cycle. As we delved into the creation of tasks, we realized that we needed to assess the learnability of our design concepts, in addition to their usability, to best direct our design efforts in the immediate future. We conducted an extensive assessment of the learnability of the application. Our testing included unstructured time for exploration of the software, participant-led training with a moderator who was knowledgeable about the software, repeated sets of virtually identical tasks, multiple breaks, and a lengthy distraction task. This testing method provided us with necessary information about which parts of the user interface needed iteration and which parts were learnable. © Springer International Publishing Switzerland 2016. Source


Arenas-Garcia J.,Charles III University of Madrid | Petersen K.B.,SAS | Camps-Valls G.,University of Valencia | Hansen L.K.,Technical University of Denmark
IEEE Signal Processing Magazine | Year: 2013

Feature extraction and dimensionality reduction are important tasks in many fields of science dealing with signal processing and analysis. The relevance of these techniques is increasing as current sensory devices are developed with ever higher resolution, and problems involving multimodal data sources become more common. A plethora of feature extraction methods are available in the literature collectively grouped under the field of multivariate analysis (MVA). This article provides a uniform treatment of several methods: principal component analysis (PCA), partial least squares (PLS), canonical correlation analysis (CCA), and orthonormalized PLS (OPLS), as well as their nonlinear extensions derived by means of the theory of reproducing kernel Hilbert spaces (RKHSs). We also review their connections to other methods for classification and statistical dependence estimation and introduce some recent developments to deal with the extreme cases of large-scale and low-sized problems. To illustrate the wide applicability of these methods in both classification and regression problems, we analyze their performance in a benchmark of publicly available data sets and pay special attention to specific real applications involving audio processing for music genre prediction and hyperspectral satellite image processing for Earth and climate monitoring. © 1991-2012 IEEE. Source


Sim K.,Institute for Infocomm Research | Yap G.-E.,Institute for Infocomm Research | Hardoon D.R.,SAS | Gopalkrishnan V.,Deloitte | And 2 more authors.
IEEE Transactions on Knowledge and Data Engineering | Year: 2013

Actionable 3D subspace clustering from real-world continuous-valued 3D (i.e., object-attribute-context) data promises tangible benefits such as discovery of biologically significant protein residues and profitable stocks, but existing algorithms are inadequate in solving this clustering problem; most of them are not actionable (ability to suggest profitable or beneficial actions to users), do not allow incorporation of domain knowledge, and are parameter sensitive, i.e., the wrong threshold setting reduces the cluster quality. Moreover, its 3D structure complicates this clustering problem. We propose a centroid-based actionable 3D subspace clustering framework, named CATSeeker, which allows incorporation of domain knowledge, and achieves parameter insensitivity and excellent performance through a unique combination of singular value decomposition, numerical optimization, and 3D frequent itemset mining. Experimental results on synthetic, protein structural, and financial data show that CATSeeker significantly outperforms all the competing methods in terms of efficiency, parameter insensitivity, and cluster usefulness.© 2013 IEEE. Source


Zhao M.,SAS | De Farias Jr. I.R.,Texas Tech University
Mathematical Programming | Year: 2013

We give new facets and valid inequalities for the separable piecewise linear optimization (SPLO) knapsack polytope. We also extend the inequalities to the case in which some of the variables are semi-continuous. Finally, we give computational results that demonstrate their efficiency in solving difficult instances of SPLO and SPLO with semi-continuous constraints. © 2012 Springer and Mathematical Optimization Society. Source

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