Cluster analysis of late Holocene pollen trends in Ontario

1991 ◽  
Vol 69 (8) ◽  
pp. 1719-1730 ◽  
Author(s):  
Ian D. Campbell ◽  
J. H. McAndrews
Keyword(s):  
Cluster Analysis ◽  
Geometric Mean ◽  
Ice Age ◽  
White Pine ◽  
Single Linkage ◽  
Complete Linkage ◽  
Similarity Indices ◽  
Species Abundances ◽  
Climatic Cooling ◽  
Past 1000 Years

Cluster analysis of Ontario pollen stratigraphies demonstrates similar regional successions during the past 1000 years. Seven character states qualitatively describe the behaviour of the pollen percentage trends for each taxon: 0, absent; 1, present with no visible trend but high noise; 2, rising through time; 3, falling through time; 4, rise–fall; 5, fall–rise; and 6, stable through time. The three similarity indices (S) used were of the form S equals the number of characters in agreement divided by the number of informative characters. The three clustering techniques used are single linkage, complete linkage, and unpaired weighted geometric mean analysis. Single linkage and unpaired weighted geometric mean analysis showed a north–south division with all three indices; complete linkage showed only rare local groupings with all three indices. The division between the two clusters falls just south of Lake Nipissing. All successions indicate climatic cooling; the clusters reflect southward movement of the centres of species abundances, particularly white pine. The method identifies regions of similar vegetation dynamics. Key words: cluster analysis, forest dynamics, Holocene, Little Ice Age, Ontario, palynology.

scholarly journals PENGELOMPOKAN DESA/KELURAHAN DI KOTA DENPASAR MENURUT INDIKATOR PENDIDIKAN

2016 ◽  
Vol 5 (2) ◽  
pp. 38
Author(s):  
NI WAYAN ARIS APRILIA A.P ◽  
I GUSTI AYU MADE SRINADI ◽  
KARTIKA SARI
Keyword(s):  
Cluster Analysis ◽  
Data Analysis ◽  
The Other ◽  
Single Linkage ◽  
Complete Linkage ◽  
Hierarchical Method ◽  
Average Linkage ◽  
Different Characteristics

Cluster analysis is one of data analysis used to classify objects in clusters which has objects with the same characteristics, whereas the other cluster has different characteristics. One part of the method of analysis cluster is hierarchy method. In a hierarchical method there are methods of linkage in the form of incorporation. Generally, methods of linkage is divided into 5 methods: single linkage, complete linkage, average linkage, Ward and centroid.  The purpose of this study was to determine the best method of linkage among the method of single linkage, complete linkage, average linkage, and Ward, using Euclidean and Pearson proximity distance. Base on the smallest value of CTM (Cluster Tightness Measure), the best method of linkage as a result of this research was average linkage in Pearson distance.

scholarly journals A COMPARISON BETWEEN SINGLE LINKAGE AND COMPLETE LINKAGE IN AGGLOMERATIVE HIERARCHICAL CLUSTER ANALYSIS FOR IDENTIFYING TOURISTS SEGMENTS

2012 ◽  
Vol 12 (6) ◽  
Author(s):  
Noor Rashidah Rashid
Keyword(s):  
Cluster Analysis ◽  
Hierarchical Cluster Analysis ◽  
Hierarchical Cluster ◽  
Single Linkage ◽  
Complete Linkage ◽  
Multivariate Method ◽  
Agglomerative Hierarchical Cluster ◽  
Linkage Methods ◽  
Agglomerative Hierarchical Cluster Analysis ◽  
Analyze Data

Cluster Analysis is a multivariate method in statistics. Agglomerative Hierarchical Cluster Analysis is one of approaches in Cluster Analysis. There are two linkage methods in Agglomerative Hierarchical Cluster Analysis which are Single Linkage and Complete Linkage. The purpose of this study is to compare between Single Linkage and Complete Linkage in Agglomerative Hierarchical Cluster Analysis. The comparison of performances between these linkage methods was shown by using Kruskal-Wallis test. The result of the comparison used for segmenting tourists of Kapas Island. The statistical software SPSS has been applied to analyze data of this research. The result from Kruskal-Wallis test shows Complete Linkage is more useful in identifying tourists segments. Keywords : Agglomerative Hierarchical Cluster Analysis, Single Linkage, Complete Linkage, Kruskal-Wallis test, tourists

scholarly journals Comparative Study of Hydrochemical Classification Based on Different Hierarchical Cluster Analysis Methods

2020 ◽  
Vol 17 (24) ◽  
pp. 9515
Author(s):  
Jianwei Bu ◽  
Wei Liu ◽  
Zhao Pan ◽  
Kang Ling
Keyword(s):  
Cluster Analysis ◽  
Hierarchical Cluster Analysis ◽  
Hierarchical Cluster ◽  
Minimum Variance ◽  
Single Linkage ◽  
Complete Linkage ◽  
Advantages And Disadvantages ◽  
Average Linkage ◽  
Analysis Methods ◽  
Hydrochemical Classification

Traditional methods for hydrochemical analyses are effective but less diversified, and are constrained to limited objects and conditions. Given their poor accuracy and reliability, they are often used in complement or combined with other methods to solve practical problems. Cluster analysis is a multivariate statistical technique that extracts useful information from complex data. It provides new ideas and approaches to hydrogeochemical analysis, especially for groundwater hydrochemical classification. Hierarchical cluster analysis is the most widely used method in cluster analysis. This study compared the advantages and disadvantages of six hierarchical cluster analysis methods and analyzed their objects, conditions, and scope of application. The six methods are: The single linkage, complete linkage, median linkage, centroid linkage, average linkage (including between-group linkage and within-group linkage), and Ward’s minimum-variance. Results showed that single linkage and complete linkage are unsuitable for complex practical conditions. Median and centroid linkages likely cause reversals in dendrograms. Average linkage is generally suitable for classification tasks with multiple samples and big data. However, Ward’s minimum-variance achieved better results for fewer samples and variables.

Genetic Diversity Assessment in Dolichos Bean (Lablab purpureus L.) Based on Principal Component Analysis and Single Linkage Cluster Analysis

2021 ◽  
Author(s):  
S.R. Singh ◽  
S. Rajan ◽  
Dinesh Kumar ◽  
V.K. Soni
Keyword(s):  
Genetic Diversity ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Single Linkage ◽  
Pod Yield ◽  
Diversity Assessment ◽  
Single Linkage Cluster ◽  
Linkage Cluster

Background: Dolichos bean occupies a unique position among the legume vegetables of Indian origin for its high nutritive value and wider climatic adaptability. Despite its wide genetic diversity, no much effort has been undertaken towards genetic improvement of this vegetable crop. Knowledge on genetic variability is an essential pre-requisite as hybrid between two diverse parental lines generates broad spectrum of variability in segregating population. The current study aims to assess the genetic diversity in dolichos genotypes to make an effective selection for yield improvement.Methods: Twenty genotypes collected from different regions were evaluated during year 2016-17 and 2017-18. Data on twelve quantitative traits was analysed using principal component analysis and single linkage cluster analysis for estimation of genetic diversity.Result: Principal component analysis revealed that first five principal components possessed Eigen value greater than 1, cumulatively contributed greater than 82.53% of total variability. The characters positively contributing towards PC-I to PC-V may be considered for dolichos improvement programme as they are major traits involved in genetic variation of pod yield. All genotypes were grouped into three clusters showing non parallelism between geographic and genetic diversity. Cluster-I was best for earliness and number of cluster/plant. Cluster-II for vine length, per cent fruit set, pod length, pod width, pod weight and number of seed /pod, cluster III for number of pods/cluster and pod yield /plant. Selection of parent genotypes from divergent cluster and component having more than one positive trait of interest for hybridization is likely to give better progenies for development of high yielding varieties in Dolichos bean.

scholarly journals Perbandingan Metode Single Linkage, Complete Linkage Dan Average Linkage dalam Pengelompokan Kecamatan Berdasarkan Variabel Jenis Ternak Kabupaten Sidoarjo

Jurnal INFORM ◽  
2019 ◽  
Vol 4 (2) ◽  
Author(s):  
Sulthan Fikri Mu'afa ◽  
Nurissaidah Ulinnuha
Keyword(s):  
Farm Animals ◽  
Single Linkage ◽  
Daily Lives ◽  
Food Ingredients ◽  
Complete Linkage ◽  
Material Sources ◽  
Average Linkage ◽  
Linkage Method ◽  
Livestock Products ◽  
Labor Resources

Livestock products are widely used by the community in their daily lives, for example as food ingredients, industrial material sources, labor resources, fertilizer sources and energy sources. This study aims to cluster livestock potential with data on livestock population in Sidoarjo Regency in 2017 with single linkage, complete linkage and average linkage method and comparing performance of the methods. In this cluster, the data will be grouped into 3 clusters. The results of the three clusters were obtained by sixteen sub-districts in the first cluster with the potential for low livestock and each one in the second and third clusters for single linkage and average linkage. While complete linkage obtained fifteen sub-districts in the first cluster with high potential for livestock, two sub-districts in the second cluster with the potential of medium livestock and one sub-district in the third cluster with the potential for high farm animals. In the comparison of the standard deviation ratio value, the smallest value of 0.222 is obtained by complete linkage, which shows that complete linkage is better than single linkage and average linkage in the case of subgrouping based on Sidoarjo regency livestock types.

scholarly journals Quantitative Measurements of Breast Density Using Magnetic Resonance Imaging: A Systematic Review and Meta-Analysis

2019 ◽  
Vol 8 (5) ◽  
pp. 745 ◽  
Author(s):  
Rooa Sindi ◽  
Cláudia Sá Dos Reis ◽  
Colleen Bennett ◽  
Gil Stevenson ◽  
Zhonghua Sun
Keyword(s):  
Systematic Review ◽  
Cluster Analysis ◽  
Breast Density ◽  
Breast Imaging ◽  
Quantitative Assessment ◽  
Nearest Neighbor ◽  
Meta Analysis ◽  
Fatty Tissue ◽  
Single Linkage ◽  
Meta Analyses

Breast density, a measure of dense fibroglandular tissue relative to non-dense fatty tissue, is confirmed as an independent risk factor of breast cancer. Although there has been an increasing interest in the quantitative assessment of breast density, no research has investigated the optimal technical approach of breast MRI in this aspect. Therefore, we performed a systematic review and meta-analysis to analyze the current studies on quantitative assessment of breast density using MRI and to determine the most appropriate technical/operational protocol. Databases (PubMed, EMBASE, ScienceDirect, and Web of Science) were searched systematically for eligible studies. Single arm meta-analysis was conducted to determine quantitative values of MRI in breast density assessments. Combined means with their 95% confidence interval (CI) were calculated using a fixed-effect model. In addition, subgroup meta-analyses were performed with stratification by breast density segmentation/measurement method. Furthermore, alternative groupings based on statistical similarities were identified via a cluster analysis employing study means and standard deviations in a Nearest Neighbor/Single Linkage. A total of 38 studies matched the inclusion criteria for this systematic review. Twenty-one of these studies were judged to be eligible for meta-analysis. The results indicated, generally, high levels of heterogeneity between study means within groups and high levels of heterogeneity between study variances within groups. The studies in two main clusters identified by the cluster analysis were also subjected to meta-analyses. The review confirmed high levels of heterogeneity within the breast density studies, considered to be due mainly to the applications of MR breast-imaging protocols and the use of breast density segmentation/measurement methods. Further research should be performed to determine the most appropriate protocol and method for quantifying breast density using MRI.

scholarly journals Analysis of Multiple-Angle Microwave Observations of Snow and Ice Using Cluster-Analysis Techniques

1981 ◽  
Vol 27 (95) ◽  
pp. 89-97 ◽  
Author(s):  
Stanley R. Rotman ◽  
Arthur D. Fisher ◽  
David H. Staelin
Keyword(s):  
Cluster Analysis ◽  
Sea Ice ◽  
Accumulation Rate ◽  
Ice Cover ◽  
Ice Age ◽  
Analysis Techniques ◽  
Brightness Temperatures ◽  
Microwave Spectrometer ◽  
Angular Data ◽  
Microwave Signatures

AbstractThe Nimbus-6 satellite carries the Scanning Microwave Spectrometer experiment (SCAMS) which continuously maps the Earth’s surface at two frequencies (22.235 and 31.65 GHz) and at six angles besides nadir. Cluster analysis was applied to these observations to determine the influence of various geophysical parameters on the radiometric brightness temperatures.Characteristic microwave signatures for a variety of terrain were obtained by this method; discrete clusters were distinguished for sea ice (with sub-classes for ice age and fractional ice cover) and firn (with accumulation-rate sub-classes). The availability of the angular data greatly facilitated separate determinations of the extent of continuous sea ice and mixtures of sea ice and water.

Identifying Rogue Air Temperature Stations Using Cluster Analysis of Percentile Trends

2005 ◽  
Vol 18 (8) ◽  
pp. 1275-1287 ◽  
Author(s):  
Scott M. Robeson ◽  
Jeffrey A. Doty
Keyword(s):  
Cluster Analysis ◽  
Air Temperature ◽  
Large Body ◽  
Large Temperature ◽  
Daily Maximum ◽  
Climatic Data ◽  
Single Linkage ◽  
Frequency Distributions ◽  
Temperature Trends ◽  
Average Linkage

Abstract A new and efficient method for identifying “rogue” air temperature stations—locations with unusually large air temperature trends—is presented. Instrumentation problems and spatially unrepresentative local climates are sometimes more apparent in air temperature extremes, yet can have more subtle impacts on variations in mean air temperature. As a result, using data from over 1300 stations in North America, the tails of daily air temperature frequency distributions were examined for unusual trends. In particular, linear trends in the 5th percentile of daily minimum air temperature during the winter months and the 95th percentile of daily maximum air temperature during the summer were analyzed. Cluster analysis then was used to identify stations that were distinct from other locations. Both single- and average linkage clustering were evaluated. By identifying individual stations along the entire periphery of the percentile trend space, single-linkage clustering appears to produce better results than that of average linkage. Average linkage clustering tends to group together several stations with large trends; however, only a handful of these stations appear distinctly different from the large body of trends toward the center of the percentile trend space. Maps of the rogue stations show that most are in close proximity to numerous other stations that were not grouped into the rogue cluster, making it unlikely that the unusually large temperature trends were due to regional climatic variations. As with all approaches for evaluating data quality, time series plots and station history information also must be inspected to more fully understand inhomogeneous variations in historical climatic data.

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