Linkage Clustering Research Articles

Variability analysis of some genotypes in Nigeria tea (Camellia sinensis) germplasm

Thirty four tea clones were sourced from Cocoa Research Institute of Nigeria tea germplasm and raised through stem cuttings for 10 months in the nursery. The experiment was laid out in a randomized complete block design (RCBD) with 3 replications in 2016. Agronomic and yield data were collected and subjected to analysis of variance. Single linkage cluster analysis (SLCA), principal component analysis (PCA) and FATCLUS analysis were employed to analyse the data. ANOVA showed considerable significant variation p<0.05 among the 34 tea genotypes. The PCA showed that Plant Height (PH) 0.39, Number of Leaves (NL) 0.38, Number of Branches (NB) 0.37, Harvestable Points (HP) 0.31, Stem Diameter 0.39 and Leaf Breadth 0.30 accounted for most of the variations observed. Axes 1, 2 and 3 of the PCA accounted for 37.23%, 15.48% and 10.75% variability respectively with cumulative value of 63.47%. The genotypes were clustered into 7 groups by FASTCLUS Analysis. The dendrogram showed that the first cluster was observed between NGC29 and NGC 47 at 0.08 level of similarity. At 0.50 level of similarity the dendrogram revealed 5 distinct groups. Therefore, variation exists among the tea germplasm in Nigeria thereby suggests further presenting opportunities for further varietal development.

Phenotypic Diversity of Ethiopian Coffee (<i>Coffea arabica </i>L.) Accessions Collected from Limmu Coffee Growing Areas Using Multivariate Analysis

Forty seven <i>Coffea arabica</i> L. germplasm accessions collected from Limmu district were field evaluated from 2004/5 to 2013/14 with two commercial check varieties at Agaro Agricultural Research sub center in single plot. The objective of the experiment was to assess the variability among the accessions using quantitative traits. Data for about eight quantitative traits were recorded only once in experimental period, while the yield data were recorded for six consecutive cropping seasons. Cluster, genetic divergence, and principal component analysis were used to assess the variability among the genotypes. The results revealed that average linkage cluster analysis for nine traits grouped the germplasm accessions in to three clusters. The number of accessions per cluster ranged from three in cluster III to 25 in cluster II. The clustering pattern of the coffee accessions revealed that the prevalence of moderate genetic diversity in Limmu coffee for the characters studied. The maximum inter-cluster distance was observed between clusters II and III; whereas the minimum inter-cluster distance was observed between clusters I and II. The study highlighted the possibility of using accessions of the distant clusters as potential candidates for the genetic improvement of Limmu coffee through hybridization. Moreover, the principal component analysis showed that the first four PCs extracted about 82% of the total variance of the 49 coffee germplasm accessions and also confirmed moderate diversity among the germplasm accessions since the entire variation cannot be explained in terms of few PCs. This, in turn, indicates that the involvement of a number of traits in contributing towards the overall diversity.

Development and evaluation of an outbreak surveillance system integrating whole genome sequencing data for non-typhoidal Salmonella in London and South East of England, 2016-17.

An outbreak surveillance system for Salmonella integrating whole genome sequencing (WGS) and epidemiological data was developed in South East and London in 2016-17 to assess local WGS clusters for triage and investigation. Cases genetically linked within a 5 single-nucleotide polymorphism (SNP) single linkage cluster were assessed using a set of locally agreed thresholds based on time, person and place, for reporting to local health protection teams (HPTs). Between September 2016 and September 2017, 230 unique 5-SNP clusters (442 weekly reports) of non-typhoidal Salmonella 5-SNP WGS clusters were identified, of which 208 unique 5-SNP clusters (316 weekly reports) were not reported to the HPTs. In the remaining 22 unique clusters (126 weekly clusters) reported to HPTs, nine were known active outbreak investigations, seven were below locally agreed thresholds and six exceeded local thresholds. A common source or vehicle was identified in four of six clusters that exceeded locally agreed thresholds. This work demonstrates that a threshold-based surveillance system, taking into account time, place and genetic relatedness, is feasible and effective in directing the use of local public health resources for risk assessment and investigation of non-typhoidal Salmonella clusters.

Linkage Analysis among Factors in Obtaining Return Quantity / Volume: An Interpretive Structural Modelling on Construction Machinery Remanufacturing Industries

Core acquisition management is one of the most crucial activities in remanufacturing industries. The availability of cores is important to support company"s operational functions. This study aims to investigate the supporting factors of a successful core acquisition, focusing on return quantity / volume. Several key factors, considered as the root of the acquisition problem, have been identified through Interpretive Structural Model (ISM) method that involves the opinions of experts. From the initial identification, we obtained nine factors, in which two factors are in the dependent cluster, five factors are in the linkage cluster, and two factors are in the independent cluster. The results of the ISM-based model and MICMAC analysis showed that relationship type (factor 1) and convenience of logistics (factor 4) are the keys to successfully obtain an adequate amount of return quantity / volume. The results showed that the ISM method helps to simplify complex problems and suggest the remanufacturing companies to focus and pay more attention to these two factors, for the success of a core acquisition strategy.

WILD SUGARCANE - SACCHARUM SP. GERMPLASM COLLECTION IN THE STATES OF PUNJAB AND HARYANA, INDIA

The Indian sub-continent harbours a great wealth of diversity of Saccharum and its related genera. Several targeted expeditions were conducted to collect the range of diversity present in Saccharum complex from diverse parts of the country. But the north western states of Punjab and Haryana were hitherto unexplored. A targeted exploration was conducted during 2016 to collect the entire range of variability present in Saccharum complex. Collections were made from a wide range of habitats and altitudes. The Saccharum spontaneum was present mostly along the river banks and river beds and showed considerable variability for morphological characteristics. S. spontaneum was distributed throughout both the states and collections could be made from all the districts and union territory of Chandigarh. A total of 104 accessions comprising of 100 Saccharum spontaneum and four Erinathus sp. have been collected. They were occurring mostly as small population except in the canal bunds where larger population was noticed. Plant height ranged from 0.46 m to 4.5 m. Variation for leaf and stem characters were observed from the collections. Statistical analysis of altitude of the collection sites with plant characters showed no significant association. Correlation analysis among plant characters showed the highest correlation coefficient between plant height and internode thickness. Clustering of Haryana, Chandigarh and Punjab Saccharum spontaneum collections through complete linkage clustering resulted in five clear clusters among the collections. Geographic distance and environmental factors including local climates interplay in the formation of the genetic structure and geographic patterns of the native S. spontaneum populations. Haryana and Punjab state were not much explored during earlier period and hence the collection would add more diversity to the germplasm maintained. In addition to the 100 S. spontaneum, four Erianthus sp. were collected during the exploration which will enrich the gene pool.

Applications of miospore quantitative stratigraphy and cyclostratigraphy in the Neocomian - Cenomanian sediments in the Western Desert, Egypt: A step in the way to biochronostratigraphy

The Lower Cretaceous section in the Western Desert witnesses regular petroleum exploration activities and contains active petroleum systems. The main tool for biostratigraphic classification of these important strata is the palynology based biozonation. The classical biozonation methods that rely on the ranges between only selected zonal palynomorphs markers or bioevents cause many problems in the correlation of the stratigraphic units. These problems slowed the development of a unified scheme and constructions of a time scale. In this study, another approach is introduced for the first time to overcome these problems. This approach depends on analytical stratigraphy that is based on a number of quantitative methods in the framework of the geo-mathematics. These quantitative methods include statistical constrained single linkage cluster analysis, graphic correlation, constrained optimization, ranking and scaling and unitary associations. These methods take into account all of the identified taxa bioevents or their co-existences. Each of these methods produces a biozonation that predict the real distribution of the palynomorphs in time and space in a different approach. In addition, these methods rank and scale the taxa bioevents in a way that can be calibrated to a time scale. The resulted biozonations are comparable through the methods. The study is based on the miospore records of 15 wells that are closely spaced to become the center for future correlations. This is an important step toward the biochronology that add the time dimension to the biostratigraphic analysis. In addition, the present study introduces for the first time the astronomical time calibration in Bassel-1 well based on the principles of cyclostratigraphy including time series analysis and age model tuning. The G-ray log of this well is tuned to 405 kyr eccentricity cycle to produce an age model that anchored on the OAEs. The age model can be developed by additional calibration and tuned to a higher resolution and used in other applications.

Determination of water quality assessment in wells of the Göksu Plains using multivariate statistical techniques

Groundwater is one of the most important sources of freshwater globally and has a key role in sustaining the ecological value of many areas. These valuable and vulnerable groundwater resources are under the pressure of many external pollution threats, such as industrial, domestic, and agricultural chemicals. To prevent undesirable consequences to future groundwater resources, comprehensive and proper knowledge of groundwater quality and contamination levels is vital at the local/regional scale. In this study, multivariate statistical techniques, such as a correlation matrix, hierarchical cluster analysis, and factor analysis, were applied to water quality data sets obtained from 24 wells located in the Göksu Plain. These techniques were applied primarily to nineteen hydrochemical parameters collected between May 2011 and April 2012. The results obtained from the correlation matrix showed that the seawater descriptors such as EC, TDS, Cl−, Na+, and K+ were strongly correlated. Principal component analysis indicated that most of the variations in groundwater were caused by four factors that were responsible for the water quality, which represented more than 80.22% of the total data variance. The hierarchical average linkage cluster analysis produced two major clusters that reflected seawater salinity.

Robust Mean-Variance Portfolio Selection with Ward and Complete Linkage Clustering Algorithm

Robust Mean-Variance Portfolio Selection with Ward and Complete Linkage Clustering Algorithm

Morphological and molecular studies of three species of Boerhavia L. from Ile-Ife, Nigeria

A wide range of plasticity and polymorphism have been documented in the genus Boerhavia. More so, the possible presence of natural hybrids among the species of the genus has made species identification more difficult. This study employed morphological characters, pollen parameters as well as Random Amplified Polymorphic DNA (RAPD) to identify and elucidate the phylogenetic relationship that exists among the three species of Boerhavia distributed in Ile-Ife, Nigeria. Qualitative and quantitative morphological characters were observed and measured respectively. The acetolysis of the pollen grains was carried out according to Erdtman method. Fresh and young leaves of the Boerhavia species studied were collected for genomic DNA extraction using modified Dellarpota procedure. The quality and concentration of DNA was assessed by gel electrophoresis on 2% agarose with known concentrations of undigested lambda DNA. Subsequently, the DNA quantification was done according to standard measurement. The Principal Components Analysis of the morphological traits and the pollen grain studies indicated that the three Boerhavia species studied have a very high level of relationship. However, the Single Linkage Cluster Analysis of the morphological data and the unweighted pair group method with arithmetic mean (UPGMA) cluster analysis of RAPD data revealed that B. erecta is distantly related to B. coccinea and B. diffusa. Despite the fact that the three Boerhavia species studied possess high level of similarity, they can still be distinguished from each other. In conclusion, B. coccinea and B. diffusa are more closely related to each other than B. erecta.&#x0D; Key words: RAPD marker, Pollen grains, Phylogenetic relationship, Polymorphism

Improving Density Peak Clustering by Automatic Peak Selection and Single Linkage Clustering

Density peak clustering (DPC) is a density-based clustering method that has attracted much attention in the academic community. DPC works by first searching density peaks in the dataset, and then assigning each data point to the same cluster as its nearest higher-density point. One problem with DPC is the determination of the density peaks, where poor selection of the density peaks could yield poor clustering results. Another problem with DPC is its cluster assignment strategy, which often makes incorrect cluster assignments for data points that are far from their nearest higher-density points. This study modifies DPC and proposes a new clustering algorithm to resolve the above problems. The proposed algorithm uses the radius of the neighborhood to automatically select a set of the likely density peaks, which are far from their nearest higher-density points. Using the potential density peaks as the density peaks, it then applies DPC to yield the preliminary clustering results. Finally, it uses single-linkage clustering on the preliminary clustering results to reduce the number of clusters, if necessary. The proposed algorithm avoids the cluster assignment problem in DPC because the cluster assignments for the potential density peaks are based on single-linkage clustering, not based on DPC. Our performance study shows that the proposed algorithm outperforms DPC for datasets with irregularly shaped clusters.

Characterization of standardized heavy rainfall profiles for Barcelona city: clustering, rain amounts and intensity peaks

Standardized heavy rainfall profiles may contribute to a best knowledge concerning flash floods, ground erosion and runoff. Taking advantage of the rain rate gauges network of Barcelona city and applying a short integration time of 5 min, the heavy rainfall records with amounts above 25 mm and longer than 60 min have been selected along 15 years, being detected 499 records corresponding to 67 episodes. The amount distribution of these records are analysed at time deciles. By grouping these standardized rainfall profiles according to their similarity by means of the average linkage clustering algorithm, 10 clusters are derived, each one of them characterized by different time evolution of rainfall. The representative standardized rainfall profiles for every one of the clusters, together with maximum 5-min rain amounts and rainfall amounts characterizing them, permit to distinguish rainfall patterns. In addition, the extreme heavy rainfalls which may lead to flash floods are identified, as also their respective synoptic situations. In short, this analysis offers a description of heavy rainfall patterns in Barcelona city, complementing previous papers on the normalized intensity curves and 5-min intensity return periods. These heavy rainfall analyses would be very useful when designing drainage and sewerage systems in urban areas as Barcelona, where flash floods may be expected due to episodes of notable rainfall amount and intensity. The imperviousness density of the soil and an appropriate sewerage structure should be adapted to mitigate the effects of these copious and intense episodes.

Quantized Residual Preference Based Linkage Clustering for Model Selection and Inlier Segmentation in Geometric Multi-Model Fitting

In this paper, quantized residual preference is proposed to represent the hypotheses and the points for model selection and inlier segmentation in multi-structure geometric model fitting. First, a quantized residual preference is proposed to represent the hypotheses. Through a weighted similarity measurement and linkage clustering, similar hypotheses are put into one cluster, and hypotheses with good quality are selected from the clusters as the model selection results. After this, the quantized residual preference is also used to present the data points, and through the linkage clustering, the inliers belonging to the same model can be separated from the outliers. To exclude outliers as many as possible, an iterative sampling and clustering process is performed within the clustering process until the clusters are stable. The experiments undertake indicate that the proposed method performs even better on real data than the some state-of-the-art methods.

Assessment of Genetic Divergence in Long Day Onion (Allium cepa L.) through Principal Component and Single Linkage Cluster Analysis

To assess the nature and magnitude of genetic diversity in long day onion germplasm by using the principal component analysis and single linkage cluster analysis an experiment was carried out with 34 onion genotypes. High coefficient of variation with wide range in traits indicated an appreciable variability in germplasm. Genotypes were classified into seven principal components having Eigen value &gt; 1, cumulatively accounted for 83.87% of total variability. Principal Component - I contributed for 24.73% of total variation for followed by principal component-II (15.27%). PC-I had high positive loading for bulb weight (0.401), marketable yield (0.338), total bulb yield (0.401) and PC-II had high positive loading for plant height (0.412), PC-III for high T.S.S. (0.276) PC -IV for A grade bulbs (0.436), PC-V for polar diameter of bulbs (0.514), PC-IV negatively loaded with purple blotch (-0.461) and PC-VII for narrow neck thickness (-0 .515). Plotting PC-I aganist PC-II differenciated CITH-O-13, CITH-O-4, CITH-O-22, CITH-O-19, CITH-O-9, CITH-O-6 and CITH-O2 as most divergent genotype.On the basis of single linkage cluster means cluster-I was most importent for average bulb weight, minimum bolters, high marketble bulb percentage high marketable and total bulb yield whereas cluster -II was important for maximum nuber of leaves/plant and minimum neck thicknes. Highest inter-cluster distance was observed between cluter II and Cluster-I(873.5% ).Most divergent genotypes with high inter cluter distance could be the most appropriate parents for crop impovement in onion.

Assessment of Genetic Divergence in Long Day Onion (Allium cepa L.) through Principal Component and Single Linkage Cluster Analysis

To assess the nature and magnitude of genetic diversity in long day onion germplasm by using the principal component analysis and single linkage cluster analysis an experiment was carried out with 34 onion genotypes. High coefficient of variation with wide range in traits indicated an appreciable variability in germplasm. Genotypes were classified into seven principal components having Eigen value &gt; 1, cumulatively accounted for 83.87% of total variability. Principal Component - I contributed for 24.73% of total variation for followed by principal component-II (15.27%). PC-I had high positive loading for bulb weight (0.401), marketable yield (0.338), total bulb yield (0.401) and PC-II had high positive loading for plant height (0.412), PC-III for high T.S.S. (0.276) PC -IV for A grade bulbs (0.436), PC-V for polar diameter of bulbs (0.514), PC-IV negatively loaded with purple blotch (-0.461) and PC-VII for narrow neck thickness (-0 .515). Plotting PC-I aganist PC-II differenciated CITH-O-13, CITH-O-4, CITH-O-22, CITH-O-19, CITH-O-9, CITH-O-6 and CITH-O2 as most divergent genotype.On the basis of single linkage cluster means cluster-I was most importent for average bulb weight, minimum bolters, high marketble bulb percentage high marketable and total bulb yield whereas cluster -II was important for maximum nuber of leaves/plant and minimum neck thicknes. Highest inter-cluster distance was observed between cluter II and Cluster-I(873.5% ).Most divergent genotypes with high inter cluter distance could be the most appropriate parents for crop impovement in onion.

Identify and Analyze the “Important Nodes and Key Linkages” of Coastal City Cluster Using Network Analysis

Xiao, W. and Wang, L., 2020. Identify and analyze the “important nodes and key linkages” of coastal city cluster using network analysis. In: Yang, Y.; Mi, C.; Zhao, L., and Lam, S. (eds.), Global Topics and New Trends in Coastal Research: Port, Coastal and Ocean Engineering. Journal of Coastal Research, Special Issue No. 103, pp. 789–792. Coconut Creek (Florida), ISSN 0749-0208.Over the past decade, more and more countries have regarded the development of marine economy as the new opportunities of economic growth. Coastal city cluster is a form of the advanced stage of city development. It covers dozens of cities, and different cities have different influences and importance, forming multi-level of network structure. The coastal city clusters as the spatial places for marine economy have been a hot topic among scholars. However, few literature probed into the key linkages and important nodes of coastal city clusters. This paper constructs the network model of coastal city cluster, then designs basic correlation tree algorithm to identify the important nodes and key linkages of coastal city clusters. Finally empirical analysis is done with Shandong Province of China as an example. The methods designed by this paper provide quantitative evidence to the strategy-making regarding the development of oceanic economy. Meanwhile, empirical studies reveal that these methods enable new and effective research approaches for the studies into the coastal city cluster.

Outlier Detection Based on Residual Histogram Preference for Geometric Multi-Model Fitting.

Geometric model fitting is a fundamental issue in computer vision, and the fitting accuracy is affected by outliers. In order to eliminate the impact of the outliers, the inlier threshold or scale estimator is usually adopted. However, a single inlier threshold cannot satisfy multiple models in the data, and scale estimators with a certain noise distribution model work poorly in geometric model fitting. It can be observed that the residuals of outliers are big for all true models in the data, which makes the consensus of the outliers. Based on this observation, we propose a preference analysis method based on residual histograms to study the outlier consensus for outlier detection in this paper. We have found that the outlier consensus makes the outliers gather away from the inliers on the designed residual histogram preference space, which is quite convenient to separate outliers from inliers through linkage clustering. After the outliers are detected and removed, a linkage clustering with permutation preference is introduced to segment the inliers. In addition, in order to make the linkage clustering process stable and robust, an alternative sampling and clustering framework is proposed in both the outlier detection and inlier segmentation processes. The experimental results also show that the outlier detection scheme based on residual histogram preference can detect most of the outliers in the data sets, and the fitting results are better than most of the state-of-the-art methods in geometric multi-model fitting.

Robust ROI localization based on image segmentation and outlier detection in finger vein recognition

Finger vein is deemed to be a promising biological trait for individual identification. However, partially due to non-uniform collection devices and non-standard collection process, original images are polluted by lots of unfavourable factors. These negative effects increase the burden on image matching. Therefore, Region of Interest (ROI) localization plays an important role in finger vein recognition. Considering that the previous techniques are not common for all kinds of images, we propose a set of methods to obtain the ROI, which is able to remove most of negative factors, preserve more vein information and keep the stability of vein feature with less cost and fewer manual thresholds. More specifically, we propose Simplified Statistical Region Merging (SSRM) with dynamical adjustment of precision parameter to segment an image into finger body and background area. Next, in order to ensure the edge be qualified and further correct the skew angle, the novel Directional Linkage Clustering Method (DLCM) and Parameter Selection (PS) are introduced. Compared with the previous work, the number of thresholds used during the whole process is reduced to only four. The identification EER in experiments is reduced to 0.0476 on all the images in three public databases, which indicates that our method is more superior than the compared methods and performs better in the individual identification.

The orbital clusters among the near-Earth asteroids

ABSTRACT Fifteen orbital clusters (associations) were identified among ${\sim }20\, 000$ near-Earth asteroids (NEAs). All associations were found with a high statistical reliability using a single linkage cluster analysis algorithm and three orbital similarity functions. The identified groups constitute a small fraction (4.74 per cent) of the entire sample. Notwithstanding, they may be hazardous to Earth and its inhabitants. As with meteoroid streams, every year Earth comes very close to the orbits of each association. In two cases (2008TC3 and 2017FU102), the distance between the asteroid’s orbit and Earth’s orbit was shorter than Earth’s radius. Among the members of the identified associations, we found 331 objects larger than the Chelyabinsk asteroid and all of them approach Earth’s orbit at a distance smaller than 0.05 au. Two of the identified groups, (4179) Toutatis and (251430) Itokawa, support the hypothesis regarding the catastrophic origins of the asteroids Toutatis and Itokawa through violent collisions. This study does not focus on the origin of the NEA associations, but rather focuses on tracing the associations. Regardless of their origin, the identified groups pose a serious threat to Earth. Hence, to facilitate their monitoring of we calculated coordinates of the theoretical radiants and the calendar dates of their potential activity.

Clustering Provinces in Indonesia based on Community Welfare Indicators

The Preamble of the 1945 Constitution of the Republic of Indonesia explicitly states that the main task of the government of the Republic of Indonesia is to advance general prosperity, to develop the nation's intellectual life, and to realize social justice for all Indonesian people. Social inequality is a problem that is still faced by Indonesian people today. To solve the problem required supporting data analysis as a basis for policy formulation. This research was conducted with the aim of clustering provinces in Indonesia based on community welfare indicators using K-Means cluster analysis. K-Means cluster analysis is chosen based on the variance value (0.101), which is smaller than the variance value in the average linkage cluster analysis (0.152). Based on data analysis, provinces in Indonesia are clustered into three where the first cluster consists of 21 provinces, the second cluster consists of 3 provinces, and the third cluster consists of 10 provinces. Each cluster has different characteristics that can be of concern to the parties concerned to overcome the social welfare gap. Besides, in order cluster results are more easily understood, visualization of results is added with a Geographic Information System (GIS) using Indonesian maps accompanied by differences in color gradations for each cluster

VIRTUAL BUYER-SELLER INTERACTION IS THE TRUE DRIVER OF ONLINE PURCHASE-INTENTION: A STUDY BY ISM APPROACH

Individuals in modern society are hard-pressed to be physically present in each and every instance. Social media made their lives easier in this regard. So, virtual interaction, if can be effective, then those individuals (on-line purchaser) may be pushed up to the threshold level of their psychology (purchase intention) to purchase on-line their desired product. In this context, the research problem is stated as “can the on-line purchase intention be driven by virtual buyer-seller interaction?” The broad objective of the study is to analyze the relationship between ‘virtual buyer-seller interaction’ and ‘online purchase intention’ through Interpretive Structural Modeling (ISM) along their respective drivers. Qualitative research is the base of this study, where literatures on various empirical and theoretical research works or studies are logically related by the authors of this study to arrive at a structural model, which is developed through Interpretive Structural Modeling (ISM). Major Findings of this study refers that virtual buyer-seller interaction with certain level effectiveness, is justified to have meaningful impact on online purchase intention having factors like customers’ engagement, perceived value, and attitude towards on-line shopping action at the level-II of the ISM. Driving factors, dependency factors, and linkage cluster is explored clearly in this study. So far as originality / contribution are concerned, virtual buyer-seller interaction is explored to be a meaningful driver of online purchase intention. Additional originality is that three parallel mediators can be justified between the above said relationships through future empirical studies, which are developed through ISM in the current study.