Len Feremans

Orcid: 0000-0001-8717-0552

According to our database1, Len Feremans authored at least 13 papers between 2016 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

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Article 
PhD thesis 
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Links

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Bibliography

2024
Pattern-based Time Series Semantic Segmentation with Gradual State Transitions.
Proceedings of the 2024 SIAM International Conference on Data Mining, 2024

2023
Efficient pattern-based anomaly detection in a network of multivariate devices.
CoRR, 2023

Efficiently Mining Frequent Representative Motifs in Large Collections of Time Series.
Proceedings of the IEEE International Conference on Big Data, 2023

2022
PETSC: pattern-based embedding for time series classification.
Data Min. Knowl. Discov., 2022

A Neighbourhood-based Location- and Time-aware Recommender System.
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
PhD thesis, 2020

Combining instance and feature neighbours for extreme multi-label classification.
Int. J. Data Sci. Anal., 2020

2019
Efficiently mining cohesion-based patterns and rules in event sequences.
Data Min. Knowl. Discov., 2019

A Framework for Pattern Mining and Anomaly Detection in Multi-dimensional Time Series and Event Logs.
Proceedings of the New Frontiers in Mining Complex Patterns - 8th International Workshop, 2019

Pattern-Based Anomaly Detection in Mixed-Type Time Series.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2019

2018
Mining Top-<i>k</i> Quantile-based Cohesive Sequential Patterns.
Proceedings of the 2018 SIAM International Conference on Data Mining, 2018

2017
Combining Instance and Feature Neighbors for Efficient Multi-label Classification.
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.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2016


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