Natural Scales in Geographical Patterns

By Telmo Menezes, Camille Roth. 1971

Subfields: Network Science & Complex Systems

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Citations in the corpus

Object-Centric Learning with Slot Attention (2020, methodology): “For a detailed description of the ARI score, see the aforementioned repository.”
NewsEmbed: Modeling News through Pre-trained Document Representations (2021, methodology): “For clustering, we apply k-means on document embeddings assuming the cluster size is known and report Adjusted Rand Index [50].”
Polarization of the Vaccination Debate on Facebook (2018, methodology): “To compare the communities detected with the various algorithms we use standard methods that compute the similarity between different community partitions by considering how the algorithms assign the nodes to each community [27].”
k-Shape: Efficient and Accurate Clustering of Time Series (2016, methodology): “We use the one nearest neighbor classification accuracy to evaluate the distance measures and the Rand Index [57] to evaluate clustering accuracy.”
Concept Generalization in Visual Representation Learning (2020, unknown): “, rand-index [46], normalized or adjusted [60] mutual information (see the supplementary material for details).”
Parallel Multi-Dimensional LSTM, With Application to Fast Biomedical Volumetric Image Segmentation (2015, unknown): “053 0.001 Simple Thresholding 0.450 17.14 0.225 IDSIA [12] 0.050 0.420 0.061 DIVE 0.048 0.374 0.058 PyraMiD-LSTM 0.047 0.462 0.062 IDSIA-SCI 0.0189 0.617 0.103 DIVE-SCI 0.0178 0.307 0.058 Rand error [22]: 1 - F-score of rand index, which measures similarity between two segmentations on the foregroun”
Finite sample change point inference and identification for high‐dimensional mean vectors (2017, methodology): “Since change point estimation can be viewed as a special case of clustering, the accuracy (consistency) can be measured by the Adjusted Rand Index (ARI) (Hubert & Arabie, 1985; Rand, 1971).”
Extracting urban functional regions from points of interest and human activities on location‐based social networks (2017, methodology): “Two popular metrics for comparing clustering results are applied in our tests: the Rand index (Rand, 1971) and normalized mutual information (NMI) (Strehl & Ghosh, 2002).”
Learning computationally efficient approximations of complex image segmentation metrics (2013, background): “…been proposed that emphasize particular types of error (e.g., over- and under-segmentation [2], topological disagreements such as region splitting and merging [3]), can be robust to noise (e.g., small boundary displacement [4], white noise [5]), or correlate well with human intuition [3], [5], [6].”
3D Semantic Parsing of Large-Scale Indoor Spaces (2016, methodology): “Given the ground truth and the parsing result, ARI considers all feasible matching between space labels and computes a weighted average of accuracy of each matching.”
Benchmarking atlas-level data integration in single-cell genomics (2020, background): “The Rand index compares the overlap of two clusterings; it considers both correct clustering overlaps while also counting correct disagreements between two clusterings 42 .”