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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.
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
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
- Instance Space Analysis for Unsupervised Outlier Detection32-41
Sevvandi Kandanaarachchi, Mario Munoz, Kate Smith-Miles
- Lessons Learned from the FIMI Workshops42-42
- Clustering Evaluation in High-Dimensional Data43-43
- 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