[PDF][PDF] Choosing DBSCAN parameters automatically using differential evolution
Over the last several years, DBSCAN (Density-Based Spatial Clustering of Applications with
Noise) has been widely applied in many areas of science due to its simplicity, robustness …
Noise) has been widely applied in many areas of science due to its simplicity, robustness …
[HTML][HTML] Generating bunkering statistics from AIS data: A machine learning approach
G Fuentes - Transportation Research Part E: Logistics and …, 2021 - Elsevier
In ship**, the optimization of the bunkering location is dependent on price, deviation from
the planned route and the cost of delays incurred by the bunkering operation itself. Despite …
the planned route and the cost of delays incurred by the bunkering operation itself. Despite …
Sampling approaches for applying DBSCAN to large datasets
D Luchi, AL Rodrigues, FM Varejão - Pattern Recognition Letters, 2019 - Elsevier
DBSCAN is a classic clustering method for identifying clusters of different shapes and isolate
noisy patterns. Despite these qualities, many articles in the literature address the scalability …
noisy patterns. Despite these qualities, many articles in the literature address the scalability …
Online clustering of evolving data streams using a density grid-based method
In recent years, a significant boost in data availability for persistent data streams has been
observed. These data streams are continually evolving, with the clusters frequently forming …
observed. These data streams are continually evolving, with the clusters frequently forming …
A Literature survey based on DBSCAN algorithms
HV Singh, A Girdhar, S Dahiya - 2022 6th International …, 2022 - ieeexplore.ieee.org
Data clustering is set of techniques for analyzing data. Data mining comes in handy when
tried to find patterns and attributes from a large amount of data. There have been various …
tried to find patterns and attributes from a large amount of data. There have been various …
Density decay graph-based density peak clustering
Z Zhang, Q Zhu, F Zhu, J Li, D Cheng, Y Liu… - Knowledge-Based …, 2021 - Elsevier
Abstract In 2014, Rodriguez and Laio proposed a famous clustering algorithm based on a
fast search and find density peaks dubbed as DPC (Rodriguez and Laio, 2014). DPC has …
fast search and find density peaks dubbed as DPC (Rodriguez and Laio, 2014). DPC has …
Unsupervised Learning in NBA Injury Recovery: Advanced Data Mining to Decode Recovery Durations and Economic Impacts
This study utilized advanced data mining and machine learning to examine player injuries in
the National Basketball Association (NBA) from 2000–01 to 2022–23. By analyzing a …
the National Basketball Association (NBA) from 2000–01 to 2022–23. By analyzing a …
A buffer-based online clustering for evolving data stream
Data stream clustering plays an important role in data stream mining for knowledge
extraction. Numerous researchers have recently studied density-based clustering algorithms …
extraction. Numerous researchers have recently studied density-based clustering algorithms …
A research on remote fracturing monitoring and decision-making method supporting smart city
H Liang, A **an, M Mao, P Ni, H Wu - Sustainable Cities and Society, 2020 - Elsevier
Based on the investigation of the development of fracturing monitoring at home and abroad,
this paper studies the mechanism of fracturing fluid injection and fractures monitoring, and …
this paper studies the mechanism of fracturing fluid injection and fractures monitoring, and …
Mr. scan: Extreme scale density-based clustering using a tree-based network of gpgpu nodes
Density-based clustering algorithms are a widely-used class of data mining techniques that
can find irregularly shaped clusters and cluster data without prior knowledge of the number …
can find irregularly shaped clusters and cluster data without prior knowledge of the number …