M
Martin Ester
Researcher at Simon Fraser University
Publications - 220
Citations - 51093
Martin Ester is an academic researcher from Simon Fraser University. The author has contributed to research in topics: Cluster analysis & Recommender system. The author has an hindex of 59, co-authored 212 publications receiving 45928 citations. Previous affiliations of Martin Ester include University of British Columbia & Ludwig Maximilian University of Munich.
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Proceedings Article
A density-based algorithm for discovering clusters a density-based algorithm for discovering clusters in large spatial databases with noise
TL;DR: In this paper, a density-based notion of clusters is proposed to discover clusters of arbitrary shape, which can be used for class identification in large spatial databases and is shown to be more efficient than the well-known algorithm CLAR-ANS.
Proceedings Article
A density-based algorithm for discovering clusters in large spatial Databases with Noise
TL;DR: DBSCAN, a new clustering algorithm relying on a density-based notion of clusters which is designed to discover clusters of arbitrary shape, is presented which requires only one input parameter and supports the user in determining an appropriate value for it.
Journal ArticleDOI
PSORTb 3.0
Nancy Yiu-Lin Yu,James R. Wagner,Matthew R. Laird,Gabor Melli,Sébastien Rey,Raymond Lo,Phuong Dao,S. Cenk Sahinalp,Martin Ester,Leonard J. Foster,Fiona S. L. Brinkman +10 more
TL;DR: This work developed PSORTb version 3.0 with improved recall, higher proteome-scale prediction coverage, and new refined localization subcategories, and evaluated the most accurate SCL predictors using 5-fold cross validation plus an independent proteomics analysis.
Proceedings ArticleDOI
A matrix factorization technique with trust propagation for recommendation in social networks
Mohsen Jamali,Martin Ester +1 more
TL;DR: A model-based approach for recommendation in social networks, employing matrix factorization techniques and incorporating the mechanism of trust propagation into the model demonstrates that modeling trust propagation leads to a substantial increase in recommendation accuracy, in particular for cold start users.
Journal ArticleDOI
Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications
TL;DR: The generalized algorithm DBSCAN can cluster point objects as well as spatially extended objects according to both, their spatial and their nonspatial attributes, and four applications using 2D points (astronomy, 3D points,biology, 5D points and 2D polygons) are presented, demonstrating the applicability of GDBSCAN to real-world problems.