Unsupervised Spatial Clustering
6 papers with code • 0 benchmarks • 1 datasets
Benchmarks
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Latest papers
Automating DBSCAN via Deep Reinforcement Learning
DBSCAN is widely used in many scientific and engineering fields because of its simplicity and practicality.
Singapore Soundscape Site Selection Survey (S5): Identification of Characteristic Soundscapes of Singapore via Weighted k-means Clustering
We then performed weighted k-means clustering on the selected locations, with weights for each location derived from previous frequencies and durations spent in each location by each participant.
Efficient Sparse Spherical k-Means for Document Clustering
Spherical k-Means is frequently used to cluster document collections because it performs reasonably well in many settings and is computationally efficient.
k-Nearest Neighbor Optimization via Randomized Hyperstructure Convex Hull
The accuracy of the proposed k-NN algorithm is 85. 71%, while the accuracy of the conventional k-NN algorithm is 80. 95% when performed on the Haberman's Cancer Survival dataset, and 94. 44% for the proposed k-NN algorithm, compared to the conventional's 88. 89% accuracy score on the Seeds dataset.
A framework for the identification and classification of homogeneous socioeconomic areas in the analysis of health care variation
We also showed that the Location Index is a more robust descriptive measure of the distribution compared to other measures of central tendency.
TDBSCAN: Spatiotemporal Density Clustering
Trajectory data generated from personal or vehicle use of GPS devices can be utilized for travel analysis and traffic information service, whereas trip segmentation is a key step toward the semantic labelling of the trajectories.