An Improved Density-Based Approach to Spatio-Textual Clustering on Social Media
Density-based spatial clustering of applications with noise (DBSCAN) is the most commonly used density-based clustering algorithm but may not be sufficient when the input data type is heterogeneous in terms of textual description. When we aim to discover clusters of geo-tagged records relevant to a...
Main Authors: | Minh D. Nguyen, Won-Yong Shin |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2019-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/8658072/ |
Similar Items
-
DIR-ST<sup>2</sup>: Delineation of Imprecise Regions Using Spatio–Temporal–Textual Information
by: Cong Tran, et al.
Published: (2018-01-01) -
DeepDBSCAN: Deep Density-Based Clustering for Geo-Tagged Photos
by: Jang You Park, et al.
Published: (2021-08-01) -
Spatiotemporal Clustering Analysis of Bicycle Sharing System with Data Mining Approach
by: Xinwei Ma, et al.
Published: (2019-05-01) -
Tri-Clustering Based Exploration of Temporal Resolution Impacts on Spatio-Temporal Clusters in Geo-Referenced Time Series
by: Xiaojing Wu, et al.
Published: (2020-03-01) -
Spatio-temporal dynamics of tuberculosis clusters in Indonesia
by: Dyah Wulan Sumekar Rengganis Wardani, et al.
Published: (2020-01-01)