Over the course of the last twenty years, research in data mining has seen a substantial increase in interest, attracting original contributions from various disciplines including computer science, statistics, operations research, and information systems. Data mining supports a wide range of applications, from medical decision making, bioinformatics, web-usage mining, and text and image recognition to prominent business applications in corporate planning, direct marketing, and credit scoring. Research in information systems equally reflects this inter- and multidisciplinary approach, thereby advocating a series of papers at the intersection of data mining and information systems research. This special issue of Annals of Information Systems contains original papers and substantial extensions of selected papers from the 2007 and 2008 International Conference on Data Mining (DMIN'07 and DMIN'08, Las Vegas, NV) that have been rigorously peer-reviewed. The issue brings together topics on both information systems and data mining, and aims to give the reader a current snapshot of the contemporary research and state of the art practice in data mining.

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 Recommendation Algorithm 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.