Bilinear Prediction using Low Rank Models -- Finding Hidden Structure in Data with Tensor Decompositions -- Turning Prediction Tools Into Decision Tools -- Overcoming obstacles to the adoption of machine learning by domain Experts -- Resolution transfer in cancer classification based on amplification patterns -- Very Short-Term Wind Speed Forecasting using Spatio-Temporal Lazy Learning -- Discovery of Parameters for Animation of Midge Swarms -- No Sentiment is an Island: Author's activity and sentiments transactions in sentiment classification -- Active Learning for Classifying Template Matches in Historical Maps -- An evaluation of score descriptors combined with non-linear models of expressive dynamics in music -- Geo-Coordinated Parallel Coordinates (GCPC): A Case Study of Environmental Data Analysis -- Generalized Shortest Path Kernel on Graphs -- Ensembles of extremely randomized trees for multi-target regression -- Clustering-Based Optimised Probabilistic Active Learning (COPAL) -- Predictive Analysis on Tracking Emails for Targeted Marketing -- Semi-supervised Learning for Stream Recommender Systems -- Detecting Transmembrane Proteins Using Decision Trees -- Change point detection for information diffusion tree -- Multi-label Classification via Multi-target Regression on Data Streams -- Periodical Skeletonization for Partially Periodic Pattern Mining -- Predicting Drugs Adverse Side-Effects using a recommender-system -- Dr. Inventor Framework: extracting structured information from scientific publications -- Predicting Protein Function and Protein-Ligand Interaction with the 3D Neighborhood Kernel -- Hierarchical Multidimensional Classification of web documents with MultiWebClass -- Evaluating the Effectiveness of Hashtags as Predictors of the Sentiment of Tweets -- On the Feasibility of Discovering Meta-Patterns from a Data Ensemble -- An Algorithm for Influence Maximization in a Two-Terminal Series -- Parallel Graph and Its Application to a Real Network -- Benchmarking Stream Clustering for Churn Detection in Dynamic Networks -- Canonical Correlation Methods for Exploring Microbe-Environment Interactions in Deep Subsurface -- KeCo: Kernel-based Online Co-agreement Algorithm -- Tree PCA for Extracting Dominant Substructures from Labeled Rooted Trees -- Enumerating Maximal Clique Sets with Pseudo-Clique Constraint.

This book constitutes the proceedings of the 17th International Conference on Discovery Science, DS 2015, held in banff, AB, Canada in October 2015. The 16 long and 12 short papers presendted together with 4 invited talks in this volume were carefully reviewed and selected from 44 submissions.  The combination of recent advances in the development and analysis of methods for discovering scienti c knowledge, coming from machine learning, data mining, and intelligent data analysis, as well as their application in various scienti c domains, on the one hand, with the algorithmic advances in machine learning theory, on the other hand, makes every instance of this joint event unique and attractive.