Concept

Association rule learning

Related publications (30)

Scalable maximal subgraph mining with backbone-preserving graph convolutions

Karl Aberer, Thanh Trung Huynh, Quoc Viet Hung Nguyen, Thành Tâm Nguyên

Maximal subgraph mining is increasingly important in various domains, including bioinformatics, genomics, and chemistry, as it helps identify common characteristics among a set of graphs and enables their classification into different categories. Existing ...

ELSEVIER SCIENCE INC2023

Tracing Embodied Narratives of Critical Thinking

Aditi Kothiyal, Shitanshu Mishra

Critical Thinking (CrT) is generally characterized as an abstract thinking process, detached from the (bodily) actions one engages in during the process. Though recent cognitive theories assert that all thinking is action-based, the embodied and distribute ...

SPRINGER INTERNATIONAL PUBLISHING AG2021

SEISMIC VULNERABILITY ASSESSMENT AT LARGE SCALE CASE STUDY: THE CANTON OF BASEL-STADT

Large-scale seismic risk assessment requires knowledge of the vulnerability of buildings. Under the action of an earthquake, buildings with different properties also behave differently. Since it is not possible to analyze each building individually, it is ...

2020

Application of Association Rules to Determine Building Typological Classes for Seismic Damage Predictions at Regional Scale: The Case Study of Basel

Pierino Lestuzzi, Yves Sylvain Gilles Reuland, Lorenzo Diana

Assessing seismic vulnerability at large scales requires accurate attribution of individual buildings to more general typological classes that are representative of the seismic behavior of the buildings sharing same attributes. One-by-one evaluation of all ...

2019

Using Educational Data From Teaching And Learning To Inform Teachers’ Reflective Educational Design In Inquiry-Based STEM Education

Denis Gillet, Maria Jesus Rodriguez Triana

Science, Technology, Engineering and Mathematics (STEM) education is recognized as a top priority for school education worldwide and Inquiry-based teaching and learning is identified as one of the most dominant approaches. To effectively engage individual ...

2019

Incremental Learning Meets Reduced Precision Networks

Hardware accelerators for Deep Neural Networks (DNNs) that use reduced precision parameters are more energy efficient than the equivalent full precision networks. While many studies have focused on reduced precision training methods for supervised networks ...

2019

Data-Efficient Decentralized Visual SLAM

Davide Scaramuzza, Titus Cieslewski

Decentralized visual simultaneous localization and mapping (SLAM) is a powerful tool for multi-robot applications in environments where absolute positioning is not available. Being visual, it relies on cheap, lightweight and versatile cameras, and, being d ...

2018

Collecting Experience Data from Remotely Hosted Learning Applications

Denis Gillet, Christophe Salzmann

The ability to integrate multiple learning applications from different organizations allows sharing resources and reducing costs in the deployment of learning systems. In this sense, Learning Tools Interoperability (LTI) is the main current leading technol ...

SPRINGER2018

Advancing climate science with knowledge-discovery through data mining

Athanasios Nenes

Global climate change represents one of the greatest challenges facing society and ecosystems today. It impacts key aspects of everyday life and disrupts ecosystem integrity and function. The exponential growth of climate data combined with Knowledge-Disco ...

2018

Robust and Efficient Data Clustering with Signal Processing on Graphs

Lionel Jérémie Martin

Data is pervasive in today's world and has actually been for quite some time. With the increasing volume of data to process, there is a need for faster and at least as accurate techniques than what we already have. In particular, the last decade recorded t ...

EPFL2018

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