EDML 2019
Evaluation and Experimental Design in Data Mining and Machine Learning

Table of Contents

• 1st Workshop on Evaluation and Experimental Design in Data Mining and Machine Learning (EDML 2019) 1 - 3
  Eirini Ntoutsi , Erich Schubert , Arthur Zimek , Albrecht Zimmermann
  
• Evaluation of Unsupervised Learning Results: Making the Seemingly Impossible Possible 4 - 4
  Ricardo J. G. B. Campello
  
• EvalNE: A Framework for Evaluating Network Embeddings on Link Prediction 5 - 13
  Alexandru Mara , Jefrey Lijffijt , Tijl De Bie
  
• Benchmarking Nearest Neighbor Search: Influence of Local Intrinsic Dimensionality and Result Diversity in Real-World Datasets 14 - 23
  Martin Aumüller , Matteo Ceccarello
  
• Context-Driven Data Mining Through Bias Removal and Incompleteness Mitigation 24 - 30
  Feras Batarseh , Ajay Kulkarni
  
• Instance Spaces for Objective Assessment of Algorithms and Benchmark Test Suites 31 - 31
  Kate Smith-Miles
  
• Instance Space Analysis for Unsupervised Outlier Detection 32 - 41
  Sevvandi Kandanaarachchi , Mario Munoz , Kate Smith-Miles
  
• Lessons Learned from the FIMI Workshops 42 - 42
  Bart Goethals
  
• Clustering Evaluation in High-Dimensional Data 43 - 43
  Milos Radovanovic
  
• Characterizing Transactional Databases for Frequent Itemset Mining 44 - 53
  Christian Lezcano , Marta Arias