Late Breaking Papers
Book of abstracts for the Late Breaking Papers is available for download here .- Tal Galili dendextend: an R package for scientific visualization of dendograms and hierarchical clustering
- Dragi Kocev, Sašo Džeroski, Ivica Dimitrovski, Michelangelo Ceci, Tomislav Šmuc and Joao Gama MAESTRA: Learning from Massive, Incompletely annotated, and Structured Data
- Klemen Kenda, Luka Stopar and Marko Grobelnik Multi-level approach to sensor streams analysis
- Mitja Luštrek and Maja Somrak Mining telemonitoring data from congestive-heart-failure patients
- Mariana Oliveira and Luis Torgo Ensembles for Time Series Forecasting
- Aljaz Osojnik and Sašo Džeroski Modeling Dynamical Systems with Data Stream Mining
- Apurva Pathak, Bidyut Kr. Patra, Ville Ollikainen and Raimo Launonen Clustering based approach for balancing accuracy and diversity in collaborative filtering
- Matic Perovšek, Nada Lavrač and Bojan Cestnik Bridging term discovery for cross-domain literature mining
- Marko Robnik-Šikonja Generator of unsupervised semi-artificial data
- Laszlo Szathmary, Petko Valtchev, Amedeo Napoli, Marton Ispany, and Robert Godin CGT: a vertical miner for frequent equivalence classes of itemsets
- Aneta Trajanov, Vladimir Kuzmanovski, Florence Leprince, Benoit Real, Alain Dutertre, Julie Maillet-Mezeray, Sašo Džeroski and Marko Debeljak Studying the drainage periods for agricultural fields with data mining: La Jaillière case study
- Takeaki Uno and Yushi Uno Mining Graph Structures Preserved Long Period
- Viivi Uurtio, Juho Rousu, Malin Bomberg and Merja Itavaara Extracting Sparse Canonical Correlations Between Microbial Communities and Deep Groundwater Geochemistry
- Anita Valmarska and Janez Demšar Analysis of citation networks
- Nina Vidmar, Nikola Simidjievski and Sašo Džeroski Predictive process-based modeling of aquatic ecosystems
- Denny Verbeeck and Hendrik Blockeel iMauve: A Fast Incremental Model Tree Learner
- Vedrana Vidulin, Tomislav Šmuc and Fran Supek Speed and Accuracy Benchmarks of Large-Scale Microbial Gene Function Prediction with Supervised Machine Learning
- Martin Žnidaršic, Senja Pollak, Dragana Miljković, Janez Kranjc and Nada Lavrač Identifying creative fictional ideas
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