Len Feremans

According to our database, Len Feremans authored at least 17 papers between 2016 and 2025.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2025

Scalable Evaluation of Rule-Based Recommender Systems: Algorithms and Benchmarks.

Len Feremans, Bart Goethals

Proceedings of the Rules and Reasoning - 9th International Joint Conference, 2025

Sequential Rule Analysis of ICU Patient Vital Signals and Alarms.

Michela Venturini, Len Feremans, Wouter De Corte, Celine Vens

Proceedings of the Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track, 2025

MotiPlus and MotiSet: Discovering the Best Set of Motiflets in Time Series.

Len Feremans, Patrick Schäfer, Wannes Meert

Proceedings of the Machine Learning and Knowledge Discovery in Databases. Research Track, 2025

2024

Pattern-based Time Series Semantic Segmentation with Gradual State Transitions.

Louis Carpentier, Len Feremans, Wannes Meert, Mathias Verbeke

Proceedings of the 2024 SIAM International Conference on Data Mining, 2024

Session-based News Recommendation Using Cohesive Patterns.

Mozhgan Karimi, Len Feremans, Boris Cule, Bart Goethals

Proceedings of the IEEE International Conference on Big Data, 2024

2023

Efficient pattern-based anomaly detection in a network of multivariate devices.

Len Feremans, Boris Cule, Bart Goethals

CoRR, 2023

Efficiently Mining Frequent Representative Motifs in Large Collections of Time Series.

Stijn J. Rotman, Boris Cule, Len Feremans

Proceedings of the IEEE International Conference on Big Data, 2023

2022

PETSC: pattern-based embedding for time series classification.

Len Feremans, Boris Cule, Bart Goethals

Data Min. Knowl. Discov., 2022

A Neighbourhood-based Location- and Time-aware Recommender System.

Len Feremans, Robin Verachtert, Bart Goethals

Proceedings of the 5th Workshop on Online Recommender Systems and User Modeling co-located with the 16th ACM Conference on Recommender Systems, 2022

2020

Mining cohesive patterns in sequences and extreme multi-label classification

Len Feremans

PhD thesis, 2020

Combining instance and feature neighbours for extreme multi-label classification.

Len Feremans, Boris Cule, Celine Vens, Bart Goethals

Int. J. Data Sci. Anal., 2020

2019

Efficiently mining cohesion-based patterns and rules in event sequences.

Boris Cule, Len Feremans, Bart Goethals

Data Min. Knowl. Discov., 2019

A Framework for Pattern Mining and Anomaly Detection in Multi-dimensional Time Series and Event Logs.

Len Feremans, Vincent Vercruyssen, Wannes Meert, Boris Cule, Bart Goethals

Proceedings of the New Frontiers in Mining Complex Patterns - 8th International Workshop, 2019

Pattern-Based Anomaly Detection in Mixed-Type Time Series.

Len Feremans, Vincent Vercruyssen, Boris Cule, Wannes Meert, Bart Goethals

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2019

2018

Mining Top-<i>k</i> Quantile-based Cohesive Sequential Patterns.

Len Feremans, Boris Cule, Bart Goethals

Proceedings of the 2018 SIAM International Conference on Data Mining, 2018

2017

Combining Instance and Feature Neighbors for Efficient Multi-label Classification.

Len Feremans, Boris Cule, Celine Vens, Bart Goethals

Proceedings of the 2017 IEEE International Conference on Data Science and Advanced Analytics, 2017

2016

Efficient Discovery of Sets of Co-occurring Items in Event Sequences.

Boris Cule, Len Feremans, Bart Goethals

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2016