Data mining: special issue in annals of information systems

Over the course of the last twenty years, there has been an increased interest in data mining. Specifically, this area is of used in relation to computer science, statistics, operations research, and information systems/management science. Data mining has applications in corporate planning, medical decision making, bioinformatics, web-usage mining, text and image recognition, direct marketing, and credit scoring. This special issue of AoIS contains selected, rigorously peer-reviewed papers from the 2007 International Conference on Data Mining (DMIN’07), which occurred June 25-28 in Las Vegas, NV. The issue brings together topics on both information systems and data mining, and gives the reader a current snapshot of the research and practice in data mining. Provides a state-of-art, up-to-the moment, view of the evolving field of datamining Special publication of the 2007 International Conference on Data Mining (DMIN 07) Features a combination of rigorously peer-reviewed theoretical research papers, as well as industrial reports, and case studies on applications INDICE: Data mining and information systems.- Response-Based Segmentation Using Finite Mixture Partial Least Squares.- Building Acceptable Classification Models.- Mining Interesting Rules Without Support Requirement.- Classification Techniques and Error Control in Logic Mining.- An Extended Study of the Discriminant Random Forest.- Prediction with the SVM using test point margins.- Effects of Oversampling versus Cost-sensitive Learning for Bayesian and SVM Classifiers.- The Impact of Small Disjuncts on Classifier Learning.- Predicting Customer Loyalty Labels in a Large Retail Database.- PCA-based Time Series Similarity Search.- Evolutionary Optimization of Least-Squares Support Vector Machines.- Genetically Evolved kNN Ensembles.- Behaviorally Founded RecommendationAlgorithm for Browsing.- Using Web Text Mining to Predict Future Events.- Avoiding Attribute Disclosure with the (Extended) p-Sensitive k-Anonymity Model.-Privacy Preserving Random Kernel Classification of Checkerboard Partitioned Data.

• ISBN: 978-1-4419-1279-4
• Editorial: Springer
• Encuadernacion: Rústica
• Páginas: 348
• Fecha Publicación: 01/12/2009
• Nº Volúmenes: 1
• Idioma: Inglés