Vol-2436
urn:nbn:de:0074-2436-7

Copyright © 2019 for the individual papers by the papers' authors. Copying permitted for private and academic purposes. This volume is published and copyrighted by its editors.

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

Proceedings of the 1st Workshop on Evaluation and Experimental Design in Data Mining and Machine Learning
co-located with SIAM International Conference on Data Mining (SDM 2019)

Calgary, Alberta, Canada, May 4th, 2019.

Edited by

Eirini Ntoutsi, Leibniz University Hannover & L3S Research Center, Germany
Erich Schubert, Technical University Dortmund, Germany
Arthur Zimek, University of Southern Denmark, Denmark
Albrecht Zimmermann, University Caen Normandy, France

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 Possible4-4
Ricardo J. G. B. Campello
EvalNE: A Framework for Evaluating Network Embeddings on Link Prediction5-13
Alexandru Mara, Jefrey Lijffijt, Tijl De Bie
Benchmarking Nearest Neighbor Search: Influence of Local Intrinsic Dimensionality and Result Diversity in Real-World Datasets14-23
Martin Aumüller, Matteo Ceccarello
Context-Driven Data Mining Through Bias Removal and Incompleteness Mitigation24-30
Feras Batarseh, Ajay Kulkarni
Instance Spaces for Objective Assessment of Algorithms and Benchmark Test Suites31-31
Kate Smith-Miles
Instance Space Analysis for Unsupervised Outlier Detection32-41
Sevvandi Kandanaarachchi, Mario Munoz, Kate Smith-Miles
Lessons Learned from the FIMI Workshops42-42
Bart Goethals
Clustering Evaluation in High-Dimensional Data43-43
Milos Radovanovic
Characterizing Transactional Databases for Frequent Itemset Mining44-53
Christian Lezcano, Marta Arias

We offer a BibTeX file for citing papers of this workshop from LaTeX.

2019-07-28: submitted by Erich Schubert, metadata incl. bibliographic data published under Creative Commons CC0
2019-09-08: published on CEUR-WS.org |valid HTML5|