Introduction

Introduction.- What Data Scientists can Learn from History.- On Line Mining of Cyclic Association Rules From Parallel Dimension Hierarchies.- PROFIT: A Projected Clustering Technique.- Multi-Label Classification with a Constrained Minimum Cut Model.- On the Selection of Dimension Reduction Techniques for Scientific Applications.- Relearning Process for SPRT In Structural Change Detection of Time-Series Data.- K-means clustering on a classifier-induced representation space: application to customer contact personalization.- Dimensionality Reduction using Graph Weighted Subspace Learning for Bankruptcy Prediction.- Click Fraud Detection: Adversarial Pattern Recognition over 5 years at Microsoft.- A Novel Approach for Analysis of 'Real World' Data: A Data Mining Engine for Identification of Multi-author Student Document Submission.- Data Mining Based Tax Audit Selection: A Case Study of a Pilot Project at the Minnesota Department of Revenue.- A nearest neighbor approach to build a readable risk score for breast cancer.- Machine Learning for Medical Examination Report Processing.- Data Mining Vortex Cores Concurrent with Computational Fluid Dynamics Simulations.- A Data Mining Based Method for Discovery of Web Services and their Compositions.- Exploiting Terrain Information for Enhancing Fuel Economy of Cruising Vehicles by Supervised Training of Recurrent Neural Optimizers.- Exploration of Flight State and Control System Parameters for Prediction of Helicopter Loads via Gamma Test and Machine Learning Techniques.- Multilayer Semantic Analysis In Image Databases.