Special issue of QREI on Data Mining just published

News

  • Author: Statistics Views
  • Date: 19 September 2014

In many of today’s data-driven applications, the main theme seems to revolve around the effective and efficient use of ‘big data’ generated in modern processes/systems. This usually requires the involvement of various disciplines such as Mathematics, Computer Science and Statistics. The term data mining is often used to describe in general the analysis efforts of large data sets and has attracted enormous interest in multiple research fields in recent years. The knowledge generated through data mining has benefited a variety of application domains. This special issue of QREI edited by Jing Li and Murat Kulahci provides a collection of recent research efforts in this area. The application domains include engineering, care, mobile networks, and finance.

thumbnail image: Special issue of QREI on Data Mining just published

Motivated by an ingot growth process in semiconductor manufacturing, the paper by Dai et al. proposes a method to monitor growth profile trajectories of unequal lengths. The paper by Zhang et al. proposes a method to estimate the remaining useful life for engineering systems with high fluctuating degradation. The paper by Zeng and Peterson develops models to mine telephone nurse triage data. These models reveal the variation in the accessibility of the triage service and the effect of weekday/weekend, which provide significant information for performance assessment and improvement of the service. Duan et al. present a semisupervised learning method to integrate low-accuracy and high-accuracy mobile device location data. The paper by Li et al. proposes an L1-regularized support vector machine and applies it to the modeling of financial early warning systems. Phaladiganon et al. propose a support vector data description method, in which the boundary constructed for differentiating novel from normal patterns considers both shape and dense region of the data. Han and Clemmensen propose a double weighted support vector regression in which one weight is added to the slack variables in the objective function and another weight to the slack variables in the constraints. This approach is shown to be able to describe the relative importance of observations and lower the influence of possible outliners. The paper by Smith et al. studies how to improve statistical process control methods for understanding and visualizing process variation in data-rich environment and creates a quality visualization toolkit for practitioners.

Related Topics

Related Publications

Related Content

Site Footer

Address:

This website is provided by John Wiley & Sons Limited, The Atrium, Southern Gate, Chichester, West Sussex PO19 8SQ (Company No: 00641132, VAT No: 376766987)

Published features on StatisticsViews.com are checked for statistical accuracy by a panel from the European Network for Business and Industrial Statistics (ENBIS)   to whom Wiley and StatisticsViews.com express their gratitude. This panel are: Ron Kenett, David Steinberg, Shirley Coleman, Irena Ograjenšek, Fabrizio Ruggeri, Rainer Göb, Philippe Castagliola, Xavier Tort-Martorell, Bart De Ketelaere, Antonio Pievatolo, Martina Vandebroek, Lance Mitchell, Gilbert Saporta, Helmut Waldl and Stelios Psarakis.