Trend analysis using agglomerative hierarchical clustering approach for time series big data

Pasupathi, Subbulakshmi; Shanmuganathan, Vimal; Madasamy, Kaliappan; Robinson, Y. Harold; Kim, Mucheol

doi:10.1007/s11227-020-03580-9

Cited by 28 publications

(11 citation statements)

References 28 publications

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“…Unlike clustering algorithms such as k-means clustering, which have randomness in the initial steps, the agglomerative hierarchical clustering algorithm considers every data point at every iteration. This algorithm has been used in disciplines such as physiology (Ray et al, 2020 ; Steiger et al, 2019 ), transportation (Pasupathi et al, 2021 ), and disaster resilience (Hong et al, 2021 ) to provide critical insights by grouping big data.…”

Section: Methodsmentioning

confidence: 99%

Community-scale big data reveals disparate impacts of the Texas winter storm of 2021 and its managed power outage

Lee

Maron²,

Mostafavi

2022

Humanit Soc Sci Commun

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Aggregated community-scale data could be harnessed to provide insights into the disparate impacts of managed power outages, burst pipes, and food inaccessibility during extreme weather events. During the winter storm that brought historically low temperatures, snow, and ice to the entire state of Texas in February 2021, Texas power-generating plant operators resorted to rolling blackouts to prevent collapse of the power grid when power demand overwhelmed supply. To reveal the disparate impact of managed power outages on vulnerable subpopulations in Harris County, Texas, which encompasses the city of Houston, we collected and analyzed community-scale big data using statistical and trend classification analyses. The results highlight the spatial and temporal patterns of impacts on vulnerable subpopulations in Harris County. The findings show a significant disparity in the extent and duration of power outages experienced by low-income and minority groups, suggesting the existence of inequality in the management and implementation of the power outage. Also, the extent of burst pipes and disrupted food access, as a proxy for storm impact, were more severe for low-income and minority groups. Insights provided by the results could form a basis from which infrastructure operators might enhance social equality during managed service disruptions in such events. The results and findings demonstrate the value of community-scale big data sources for rapid impact assessment in the aftermath of extreme weather events.

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Section: Methodsmentioning

confidence: 99%

Community-scale big data reveals disparate impacts of the Texas winter storm of 2021 and its managed power outage

Lee

Maron²,

Mostafavi

2022

Humanit Soc Sci Commun

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“…In the AHC method, each observation started with its cluster and then followed by merging and combining several clusters into a larger one [26]. In AHC method, the computational complexity is decreased by the distance function that could utilize the clustering concept through segregating the dataset into ta total amount of cluster until it becomes a solitary attribute [27]. This will be counted as one step in the hierarchy.This method is continuously used until only one group remains or until the minimum number of groups is reached.…”

Section: Hierarchical Clusteringmentioning

confidence: 99%

Analysing Kinship in Severe Acute Respiratory Syndrome Coronavirus 2 DNA Sequences Based on Hierarchical and K-Means Clustering Methods Using Multiple Encoding Vector

Banjarnahor¹,

Bustamam²,

Siswantining³

et al. 2022

Int. J. Adv. Sci. Eng. Inf. Technol.

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Based on the World Health Organization data obtained in mid-April 2021, Coronavirus or Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has already infected more than 134.9 million people worldwide. The virus attacks human breathing, which can cause lung infections and even death. More than 2.9 million people worldwide have died due to coronavirus infection. Meanwhile, more than 1.5 million people in Indonesia have been infected, and 42.5 thousand died because of this coronavirus. Based on this data, carrying out a kinship analysis of the coronavirus is important to reduce its spread. Identifying the kinship of the COVID-19 virus and its spread can be done by forming a phylogenetic tree and clustering. This study uses the Multiple Encoding Vector method in analyzing the sequences and Euclidean distance to determine the distance matrix. This research will then use the Hierarchical clustering method to determine the number of initial centroids, which will be used later by the K-Means clustering method kinship in the SARS-CoV-2 DNA sequence. This study took samples of DNA sequences of SARS-CoV-2 from several infected countries. From the simulation results, the ancestors of SARS-CoV-2 came from China. The analysis results also show that the closest ancestors of COVID-19 to Indonesia came from India. The SARS-CoV-2 DNA sequence also consisted of nine clusters, and the sixth cluster has the greatest number of members.

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“…Indeed, the variety of factors and accident kinds that occur in various contexts makes it difficult to analyze traffic accident datasets [13,14]. Generally, because of the variety of traffic accident data such as it can include both numerical and categorical types, some important relationships may be obscured.…”

Section: Introductionmentioning

confidence: 99%

Urban Traffic Accident Features Investigation to Improve Urban Transportation Infrastructure Sustainability by Integrating GIS and Data Mining Techniques

Le,

Tran,

2023

Sustainability

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Urban traffic accidents pose significant challenges to the sustainability of transportation infrastructure not only in Vietnam but also all over the world. To decrease the frequency of accidents, it is crucial to analyze accident data to determine the relationship between accidents and causes, especially for serious accidents. This study suggests an integrated approach using Geographic Information System (GIS) and Data Mining methods to investigate the features of urban traffic accidents in Hanoi, Vietnam aiming to solve these challenges and enhance the safety and efficiency of urban transportation. Firstly, the dataset was segmented into homogenous clusters using the two-step cluster method. Secondly, the correlation between causes and traffic accidents was examined on the overall dataset as well as on each cluster using the association rule mining (ARM) technique. Finally, the location of accident groups and high-frequency sites of accidents (hotspots) were determined by using GIS techniques. As a result, a five-cluster model was created, which corresponded to five common accident groupings in Hanoi. Moreover, the results of the study also identified the types of accidents, the main causes, the time, and the surrounding areas corresponding to each accident group. In detail, cluster 5 depicted accidents on streets, provincial, and national roads caused by motorbikes making up the highest percentage within the groups, accounting for 29.2%. Speeding and driving in the wrong lane in the afternoon and at night were the main causes in this cluster (Cf ≥ 0.9 and Lt ≥ 1.22). Next, cluster 2 had the second-highest proportion. Cluster 2 presented accidents between a truck/car and a motorbike on national and provincial roads, accounting for 27.8%. Cluster 1 presented accidents between a truck/car and a motorbike on local streets, accounting for 22%. Cluster 3 illustrated accidents between two motorbikes on the country lanes, accounting for 12.3%. Finally, cluster 4 depicted single-vehicle motorbike crashes, with the lowest rate of 8.8%. More importantly, this study also recommended using repeatability criteria for the same type of accidents or causes to determine the location of hotspots. Also, suggestions for improving traffic infrastructure sustainability were proposed. To our knowledge, this is the first time in which these three methods are applied simultaneously for analyzing traffic accidents.

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Trend analysis using agglomerative hierarchical clustering approach for time series big data

Cited by 28 publications

References 28 publications

Community-scale big data reveals disparate impacts of the Texas winter storm of 2021 and its managed power outage

Community-scale big data reveals disparate impacts of the Texas winter storm of 2021 and its managed power outage

Analysing Kinship in Severe Acute Respiratory Syndrome Coronavirus 2 DNA Sequences Based on Hierarchical and K-Means Clustering Methods Using Multiple Encoding Vector

Urban Traffic Accident Features Investigation to Improve Urban Transportation Infrastructure Sustainability by Integrating GIS and Data Mining Techniques

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