Applied Data Science track -- A Novel Framework for Online Sales Burst Prediction -- Analyzing Granger causality in climate data with time series classification methods -- Automatic Detection and Recognition of Individuals in Patterned Species -- Boosting Based Multiple Kernel Learning and Transfer Regression for Electricity Load Forecasting -- CREST - Risk Prediction for Clostridium Difficile Infection Using Multimodal Data Mining -- DC-Prophet: Predicting Catastrophic Machine Failures in DataCenters -- Disjoint-Support Factors and Seasonality Estimation in E-Commerce -- Event Detection and Summarization using Phrase Networks: PhraseNet -- Generalising Random Forest Parameter Optimisation to Include Stability and Cost -- Have It Both Ways - from A/B Testing to A&B Testing with Exceptional Model Mining -- Koopman spectral kernels for comparing complex dynamics: Application to multiagent sport plays -- Modeling the Temporal Nature of Human Behavior for Demographics Prediction -- MRNet-Product2Vec: A Multi-task Recurrent Neural Network for Product Embeddings -- Optimal client recommendation for market makers in illiquid financial products -- Predicting Self-reported Customer Satisfaction of Interactions with a Corporate Call Center -- Probabilistic Inference of Twitter Users' Age based on What They Follow -- Quantifying Heterogeneous Causal Treatment Effects in World Bank Development Finance Projects -- RSSI Based Supervised Learning for Uncooperative Direction-Finding -- Sequential Keystroke Behavioral Biometrics for User Identification via Multi-view Deep Learning -- Session-Based Fraud Detection in Online E-Commerce Transactions Using Recurrent Neural Networks -- SINAS: Suspect Investigation Using Offenders' Activity Space -- Stance Classification of Tweets using Skip Char NGrams -- Structural Semantic Models for Automatic Analysis of Urban Areas -- Taking it for a Test Drive: A Hybrid Spatio-temporal Model for Wildlife Poaching Prediction Evaluated through a Controlled Field Test -- Unsupervised signature extraction from forensic logs -- Urban Water Flow and Water Level Prediction based on Deep Learning -- Using Machine Learning for Labour Market Intelligence -- Nectar track -- Activity-Driven Influence Maximization in Social Networks -- An AI Planning System for Data Cleaning -- Comparing hypotheses on sequential behavior: A Bayesian approach and its applications -- Data-driven Approaches for Smart Parking -- Image representation, annotation and retrieval with predictive clustering trees -- Music Generation Using Bayesian Networks -- Phenotype Inference from Text and Genomic Data -- Process-based Modeling and Design of Dynamical Systems -- QuickScorer: Efficient Traversal of Large Ensembles of Decision Trees -- Recent Advances in Kernel-Based Graph Classification -- Demo track -- ASK-the-Expert: Active learning based knowledge discovery using the expert -- Delve: A Data set Retrieval and Document Analysis System -- Framework for Exploring and Understanding Multivariate Correlations -- Lit@EVE: Explainable Recommendation based on Wikipedia Concept Vectors -- Monitoring Physical Activity and Mental Stress using Wrist-worn Device and a Smartphone -- Tetrahedron: Barycentric Measure Visualizer -- TF Boosted Trees: A scalable TensorFlow based framework for gradient boosting -- TrajViz: A Tool for Visualizing Patterns and Anomalies in Trajectory -- TrAnET: Tracking and Analyzing the Evolution of Topics in Information Networks -- WHODID: Web-based interface for Human-assisted factory Operations in fault Detection, Identification and Diagnosis.

The three volume proceedings LNAI 10534 - 10536 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2017, held in Skopje, Macedonia, in September 2017. The total of 101 regular papers presented in part I and part II was carefully reviewed and selected from 364 submissions; there are 47 papers in the applied data science, nectar and demo track. The contributions were organized in topical sections named as follows: Part I: anomaly detection; computer vision; ensembles and meta learning; feature selection and extraction; kernel methods; learning and optimization, matrix and tensor factorization; networks and graphs; neural networks and deep learning. Part II: pattern and sequence mining; privacy and security; probabilistic models and methods; recommendation; regression; reinforcement learning; subgroup discovery; time series and streams; transfer and multi-task learning; unsupervised and semisupervised learning. Part III: applied data science track; nectar track; and demo track.