Clustering-Based Approaches To
The Exploration of Spatio-Temporal Data

Wu, Xiaoshan; Zurita-Milla, Raúl; Kraak, M.J.; Izquierdo‐Verdiguier, Emma

doi:10.5194/isprs-archives-xlii-2-w7-1387-2017

Cited by 7 publications

(2 citation statements)

References 23 publications

(19 reference statements)

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“…Some scholars have designed this method based on the complex mode of clustering algorithms and the characteristics of traffic conditions. Wu et al proposed two clustering algorithms for complex pattern analysis: the Bregman block average co-clustering algorithm with Idivergence (BBAC-I), which enables the concurrent analysis of spatio-temporal patterns in a 2D data matrix, and the Bregman cube average tri-clustering algorithm with I-divergence (BCAT-I), which enables a complete partitional analysis in a 3D data cube [42]. Wei et al presented an improved spatio-temporal Moran scatterplot (STMS) and, as the basis of it, a novel spatio-temporal clustering method that combined the pre-classification of traffic conditions [43].…”

Section: Related Workmentioning

confidence: 99%

Taxi Passenger Hot Spot Mining Based on a Refined K-Means++ Algorithm

Wang

Ren

2021

IEEE Access

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With the development of information technology, it is possible to explore the spatialtemporal distribution characteristics of taxi travel demand by examining taxi GPS location data in order to master the actual supply and demand levels of different hot spots at different time periods. At present, in hot spot mining, the existing research on the clustering of passenger hot spots has some performance problems, such as insufficient clustering accuracy and high algorithm time complexity. The purpose of this paper is to propose a two-level subdivision concept and improve the K-means++ algorithm to finish the fine clustering of hot spots of taxi passengers. The first-level subdivision establishes a dynamic adjustable region with time and geographical range. In the second layer, a Gaussian mixture model is used for the data distribution statistics, and the optimal subdivision area number is determined according to the minimum principle of the Akaike information criterion and Bayesian information criterion. The SSE (sum of the square distance errors) is used to determine the optimal cluster number k for each local area. Finally, the K-means++ algorithm is used to complete the clustering of each local area. A week of green taxi data from New York City was used to validate the method and compare it to the traditional K-means and DBSCAN approaches. The proposed method achieved better accuracy with comparable time consumption. This demonstrated the value of the approach for hot spot data mining although clustering still has some important advantages. In addition, the hot spots in the morning peak and weekend are displayed visually, which is helpful to provide the guidance for urban transportation and planning.

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Section: Related Workmentioning

confidence: 99%

Taxi Passenger Hot Spot Mining Based on a Refined K-Means++ Algorithm

Wang

Ren

2021

IEEE Access

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“…Guigourès et al [ 23 ] proposed a technique to analyze time-varying graphs, illustrating it in the London bike-sharing system. Wu et al [ 24 , 25 ] used triclustering to analyze meteorological data from Duch weather stations. Melgar-García et al [ 26 ] applied a triclustering-based algorithm to discover patterns over time in maize crops in Portugal to help farmers improve their harvests.…”

Section: Introductionmentioning

confidence: 99%

Biclustering fMRI time series: a comparative study

2022

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Background The effectiveness of biclustering, simultaneous clustering of rows and columns in a data matrix, was shown in gene expression data analysis. Several researchers recognize its potentialities in other research areas. Nevertheless, the last two decades have witnessed the development of a significant number of biclustering algorithms targeting gene expression data analysis and a lack of consistent studies exploring the capacities of biclustering outside this traditional application domain. Results This work evaluates the potential use of biclustering in fMRI time series data, targeting the Region × Time dimensions by comparing seven state-in-the-art biclustering and three traditional clustering algorithms on artificial and real data. It further proposes a methodology for biclustering evaluation beyond gene expression data analysis. The results discuss the use of different search strategies in both artificial and real fMRI time series showed the superiority of exhaustive biclustering approaches, obtaining the most homogeneous biclusters. However, their high computational costs are a challenge, and further work is needed for the efficient use of biclustering in fMRI data analysis. Conclusions This work pinpoints avenues for the use of biclustering in spatio-temporal data analysis, in particular neurosciences applications. The proposed evaluation methodology showed evidence of the effectiveness of biclustering in finding local patterns in fMRI time series data. Further work is needed regarding scalability to promote the application in real scenarios.

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A Composite Grid Clustering Algorithm Based on Density and Balance Degree

Guo

Cai

2023

Lecture Notes in Computer Science

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Clustering-Based Approaches To The Exploration of Spatio-Temporal Data

Cited by 7 publications

References 23 publications

Taxi Passenger Hot Spot Mining Based on a Refined K-Means++ Algorithm

Taxi Passenger Hot Spot Mining Based on a Refined K-Means++ Algorithm

Biclustering fMRI time series: a comparative study

A Composite Grid Clustering Algorithm Based on Density and Balance Degree

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