Optimal Clustering and Cluster Identity in Understanding High-Dimensional Data Spaces with Tightly Distributed Points

Chikumbo, Oliver; Granville, V.

doi:10.3390/make1020042

Cited by 10 publications

(3 citation statements)

References 25 publications

(46 reference statements)

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“…The mean time each hen spent at each of the four different areas was used as the input for the calculation of the Calinski-Harabasz criterion. The Calinski-Harabasz criterion is defined in Equation ( 1) [19,20], as follows:…”

Section: Cluster Optimisationmentioning

confidence: 99%

Characterising Free-Range Layer Flocks Using Unsupervised Cluster Analysis

Sibanda

Welch

Schneider

et al. 2020

Animals

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This study aimed to identify sub-populations of free-range laying hens and describe the pattern of their resource usage, which can affect hen performance and welfare. In three commercial flocks, 3125 Lohmann Brown hens were equipped with radio-frequency identification (RFID) transponder leg bands and placed with their flock companions, resulting in a total of 40,000 hens/flock. Hens were monitored for their use of the aviary system, including feeder lines, nest boxes, and the outdoor range. K-means and agglomerative cluster analysis, optimized with the Calinski-Harabasz Criterion, was performed and identified three clusters. Individual variation in time duration was observed in all the clusters with the highest individual differences observed on the upper feeder (140 ± 1.02%) and the range (176 ± 1.03%). Hens of cluster 1 spent the least amount time on the range and the most time on the feed chain located at the upper aviary tier (p < 0.05). We conclude that an uneven load on the resources, as well as consistent and inconsistent movement patterns, occur in the hen house. Further analysis of the data sets using classification models based on support vector machines, artificial neural networks, and decision trees are warranted to investigate the contribution of these and other parameters on hen performance.

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Section: Cluster Optimisationmentioning

confidence: 99%

Characterising Free-Range Layer Flocks Using Unsupervised Cluster Analysis

Sibanda

Welch

Schneider

et al. 2020

Animals

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“…Clustering methods divide the objects into specific groups, with the goal that all data objects assigned to the same cluster have common characteristics, while different clusters have distinct characteristics (Darby, 2005). The cluster-ing methods have been widely used in climate and environmental research (Bardossy et al, 1995;Cavazos, 2000;Luo and Lau, 2017;Bernier et al, 2019).…”

Section: K-means Clusteringmentioning

confidence: 99%

Clustering diurnal cycles of day-to-day temperature change to understand their impacts on air quality forecasting in mountain-basin areas

et al. 2021

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Abstract. Air pollution is substantially modulated by meteorological conditions, and especially their diurnal variations may play a key role in air quality evolution. However, the behaviors of temperature diurnal cycles along with the associated atmospheric condition and their effects on air quality in China remain poorly understood. Here, for the first time, we examine the diurnal cycles of day-to-day temperature change and reveal their impacts on winter air quality forecasting in mountain-basin areas. Three different diurnal cycles of the preceding day-to-day temperature change are identified and exhibit notably distinct effects on the day-to-day changes in atmospheric-dispersion conditions and air quality. The diurnal cycle with increasing temperature obviously enhances the atmospheric stability in the lower troposphere and suppresses the development of the planetary boundary layer, thus deteriorating the air quality on the following day. By contrast, the diurnal cycle with decreasing temperature in the morning is accompanied by a worse dispersion condition with more stable atmosphere stratification and weaker surface wind speed, thereby substantially worsening the air quality. Conversely, the diurnal cycle with decreasing temperature in the afternoon seems to improve air quality on the following day by enhancing the atmospheric-dispersion conditions on the following day. The findings reported here are critical to improve the understanding of air pollution in mountain-basin areas and exhibit promising potential for air quality forecasting.

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“…The Elbow standard calculates the sum of squared errors (SSE) to determine the number of clusters. As the number of clusters increases, SSE decreases accordingly; when the decline slows down, an inflection point arises, and the cluster value at the inflection point is used as the optimal number of clusters [50]. The Calinsky criterion evaluates the local optimal clustering number by calculating the SSE between groups and the SSE within a group.…”

Section: Drought Spatial Distribution Pattern Identificationmentioning

confidence: 99%

Framework for Monitoring the Spatiotemporal Distribution and Clustering of Drought Characteristics in Hunan Province

Huang

Baig

et al. 2021

Applied Sciences

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Drought is a widespread phenomenon in the context of global climate change. Owing to the geographical location of Hunan Province in the middle reaches of Yangtze River and the abundance of forests area in this region with a large population, there is a need to focus on the impacts of drought for devising policies. The spatiotemporal distribution scheme of a given area must be determined to plan water management and protect ecosystems effectively. This study proposes a framework for exploring the spatiotemporal distribution model of drought using comprehensive surveys of historical meteorological stations, which consists of two parts, namely the characteristics of drought extraction in the spatiotemporal distribution and drought models discovered by the clustering method. Firstly, we utilized the run theory to extract drought characteristics, such as drought duration, drought severity, and drought intensity. Secondly, the K-means clustering method was adopted to explore the distribution patterns on the basis of the drought characteristics. Lastly, the method was applied to Hunan Province. Results show that historical drought conditions can be monitored with their characteristics of spatiotemporal variability. Three drought distribution clusters exist in this region. Cluster 1 in western Hunan tends to be a long-term, low-intensity drought, cluster 2 in the southern part tends to be a short-term, high-intensity drought, and cluster 3 in the central part is prone to severe drought. The proposed framework is flexible as it allows parameters to be adjusted and extraction methods to achieve reasonable results for a given area.

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Optimal Clustering and Cluster Identity in Understanding High-Dimensional Data Spaces with Tightly Distributed Points

Cited by 10 publications

References 25 publications

Characterising Free-Range Layer Flocks Using Unsupervised Cluster Analysis

Characterising Free-Range Layer Flocks Using Unsupervised Cluster Analysis

Clustering diurnal cycles of day-to-day temperature change to understand their impacts on air quality forecasting in mountain-basin areas

Framework for Monitoring the Spatiotemporal Distribution and Clustering of Drought Characteristics in Hunan Province

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