MDST-Dbscan
A Density-Based Clustering Method for Multidimensional Spatiotemporal Data
Bibliographic Data
| ID | 22034901 |
|---|---|
| Authors | Changlock Choi (0000-0003-1742-4937, Kyung Hee University), Seong‐Yun Hong (0000-0001-5049-8810, Kyung Hee University, corresponding author) |
| Year | 2021 |
| Volume | 10 |
| Issue | 6 |
| Pages | 391 |
| Publication date | 2021-06-06 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ISPRS International Journal of Geo-Information (JOURNAL) |
| Journal identifiers | ISSN: 2220-9964 • E-ISSN: 2220-9964 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/ijgi10060391 |
| OpenAlex | W3168494198 |
| Language | EN |
| Citations received | 3 |
| References cited | 24 |
The increasing use of mobile devices and the growing popularity of location-based ser-vices have generated massive spatiotemporal data over the last several years. While it provides new opportunities to enhance our understanding of various urban dynamics, it poses challenges at the same time due to the complex structure and large-volume characteristic of the spatiotemporal data. To facilitate the process and analysis of such spatiotemporal data, various data mining and clustering methods have been proposed, but there still needs to develop a more flexible and computationally efficient method. The purpose of this paper is to present a clustering method that can work with large-scale, multidimensional spatiotemporal data in a reliable and efficient manner. The proposed method, called MDST-DBSCAN, is applied to idealized patterns and a real data set, and the results from both examples demonstrate that it can identify clusters accurately within a reasonable amount of time. MDST-DBSCAN performs well on both spatial and spatiotemporal data, and it can be particularly useful for exploring massive spatiotemporal data, such as detailed real estate transactions data in Seoul, Korea
Cluster analysis · CURE data clustering algorithm · Data mining · DBSCAN · Fuzzy clustering · Remote sensing · Spatial analysis · Computer Science · Data Management and Algorithms · Geographic Information Systems Studies · Human Mobility and Location-Based Analysis · Artificial Intelligence
Cluster Analysis
Algorithm as 136
St-Dbscan
DBSCAN Revisited, Revisited
Data clustering
GeoAI
Methodology Review
Hierarchical Grouping to Optimize an Objective Function
Space-Time Hierarchical Clustering for Identifying Clusters in Spatiotemporal Point Data
Urban analytics defined
Understanding cities with machine eyes
Citizens as sensors
| Citations per year | 0.75 |
|---|---|
| Citation span | 2022 - 2025 (4) |
| Highly cited | No |