- Ntsys Cluster Analysis Definition
- Ntsys Cluster Analysis Meaning
Nihar Ranjan Chattopadhyay, in Induced Fish Breeding, 2017 10.3.7 Cluster Analysis UPGMA clustering method was used to generate a dendrogram for the two stocks of C. Catla in Orissa in the present study by computing the GS values with DICE coefficient in NTSYSpc 2.2 program. The dendrogram showed one cluster with the Puri and Ganjam stocks. موضوع: مهندسی, نرم افزار کاربردی جمعه 6 مرداد 1396; 0 نظر; آپدیت. نرم افزار NTsys یک نرم افزار بسیار قدرتمند جهت انجام چندین آزمون آماری چند متغیره نظیر تجزیه به مؤلفه های اصلی، Correspondence analysis، تجزیه همبستگی های کانونی و چند.
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Molecular evolution and diversity in Bacillus anthracis as detected by amplified fragment length polymorphism markers
'... anthracis as detected by amplified fragment ...'
Abstract - Cited by 55 (11 self) - Add to MetaCart
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2004. Ecological significance of microdiversity: identical 16S rRNA gene sequences can be found in bacteria with highly divergent genomes and ecophysiologies
'... A combination of cultivation-based methods with a molecular biological approach was used to investigate whether planktonic bacteria with identical 16S rRNA gene sequences can represent distinct eco- and genotypes. A set of 11 strains of Brevundimonas alba were isolated from a bacterial freshwater co ...'
Abstract - Cited by 45 (1 self) - Add to MetaCart
A combination of cultivation-based methods with a molecular biological approach was used to investigate whether planktonic bacteria with identical 16S rRNA gene sequences can represent distinct eco- and genotypes. A set of 11 strains of Brevundimonas alba were isolated from a bacterial freshwater community by conventional plating or by using a liquid most-probable-number (MPN) dilution series. These strains had identical 16S rRNA gene sequences and represented the dominant phylotype in the plateable fraction, as well as in the highest positive dilutions of the MPN series. However, internally transcribed spacer and enterobacterial repetitive intergenic consensus PCR fingerprinting analyses, as well as DNA-DNA hybridization analyses, revealed great genetic diversity among the 11 strains. Each strain utilized a specific combination of 59 carbon substrates, and the niche overlap indices were low, suggesting that each strain occupied a different ecological niche. In dialysis cultures incubated in situ, each strain had a different growth rate and cell yield. We thus demonstrated that the B. alba strains represent distinct populations with genetically determined adaptations and probably occupy different ecological niches. Our results have implications for assessment of the diversity and biogeography of bacteria and increase the perception of natural diversity beyond the level of 16S rRNA gene sequences. Analysis of 16S rRNA gene sequences has become the pri-
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Molecular phylogeny, systematics and mor phological character evolution in the Balkan Rissooidea (Cae nogastropoda
'... Sadleriana, Trichonia, Ventrosia) are discussed and illustrated based on the literature and, where necessary, on the presented additional data. These include shell macrocharacters, protoconch sculpture, soft part morphol-ogy and pigmentation, radulae, stomach, female reproductive organs, male reprod ...'
Abstract - Cited by 13 (6 self) - Add to MetaCart
Sadleriana, Trichonia, Ventrosia) are discussed and illustrated based on the literature and, where necessary, on the presented additional data. These include shell macrocharacters, protoconch sculpture, soft part morphol-ogy and pigmentation, radulae, stomach, female reproductive organs, male reproductive organs. Based on partial sequences of the ribosomal 18S RNA gene, a molecular phylogeny is presented for all the genera, and based on fragments of CO1 gene in mitochondrial DNA, for all except six genera. Based on the Adams con-sensus tree the two gene phylogenies are summarised and systematics of the group is proposed. Adrioinsulana is considered a junior synonym of Pseudamnicola; Parabythinella a junior synonym of Marstoniopsis; a new name: Radomaniola n. gen. is proposed as a replacement name for the preoccupied Orientalina. Litthabitella, morpho-logically and molecularly distinct from the hydrobioids, probably belongs to the Assimineidae. Marstoniopsis belongs to the Amnicolidae, Bythinella to Bythinellidae, Lithoglyphus to Lithoglyphidae, Heleobia to Cochlio-pidae, Bithynia and Parabithynia to Bithyniidae, Emmericia to Emmericiidae. Paladilhiopsis and Bythiospeum be-long to the Moitessieriidae, there being no reason for homologising the two genera. All the other genera be-long to the monophyletic family Hydrobiidae, within which two subfamilies can be distinguished: Hydrobiinae and Sadlerianinae. The latter includes mostly very closely related genera, which makes splitting of this subfamily into more groups of this rank unjustified. The phylogeny of the molecular characters is mapped on
Geometric Morphometrics and Phylogeny
Ntsys Cluster Analysis Definition
'... This paper reviews some of the important properties of geometric morphometric shape variables and discusses the advantages and limitations of the use of such data in studies of phylogeny. A method for fitting morphometric data to a phylogeny (i.e., estimating ancestral states of the shape variables) ...'
Abstract - Cited by 9 (0 self) - Add to MetaCart
This paper reviews some of the important properties of geometric morphometric shape variables and discusses the advantages and limitations of the use of such data in studies of phylogeny. A method for fitting morphometric data to a phylogeny (i.e., estimating ancestral states of the shape variables) is presented using the squared-change parsimony criterion for estimation. These results are then used to illustrate shape change along a phylogeny as a deformation of the shape of any other node on the tree (e.g., the estimated root of the tree). In addition, a method to estimate the digitized image of an ancestor is given that uses averages of unwarped images. An example dataset with 18 wing landmarks for 11 species of mosquitoes is used to illustrate the methods.
Spatial variation in the frequency and intensity of antibiotic interactions among streptomycetes from prairie soil. Appl Environ Microbiol 70: 1051–1058
'... Antibiotic interactions are believed to be significant to microbial fitness in soil, yet little is known of the frequency, intensity, and diversity of antibiotic inhibition and resistance among indigenous microbes. To begin to address these issues, we studied the abilities of streptomycete isolates ...'
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Antibiotic interactions are believed to be significant to microbial fitness in soil, yet little is known of the frequency, intensity, and diversity of antibiotic inhibition and resistance among indigenous microbes. To begin to address these issues, we studied the abilities of streptomycete isolates from prairie soil to inhibit growth and display resistance to antibiotics produced by a test collection of 10 streptomycete isolates. Wide variations in antibiotic inhibition and resistance for prairie isolates among three locations and four soil depths within a 1-m2 plot were revealed. Fewer than 10 % of 153 prairie isolates inhibited all 10 test isolates, while more than 40 % of the isolates did not inhibit any of the test isolates. No field isolate was resistant to all of the test isolates, nor was any isolate susceptible to all of the test isolates. No correlation between inhibition and resistance phenotypes was found, suggesting that inhibition and resistance are under independent selection. The signif-icant spatial variation in the frequency and intensity of antibiotic inhibition implies that the fitness benefits of antibiotic production are not the same among locations in soil. In contrast, the consistency of resistance over space indicates that its significance to fitness across locations is stable or the costs of maintaining resistance in the absence of selection are small or nonexistent. The spatial clustering of antibiotic inhibitory activity suggests a variable matrix of selection pressures and microbial responses across the soil landscape. Although antibiotic activity may significantly affect interac-
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DArT markers for the rye genomegenetic diversity and mapping. BMC Genomics. doi
'... ...'
Abstract - Cited by 7 (3 self) - Add to MetaCart
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DNA hybridization evidence for the principal lineages of hummingbirds (Aves:Trochilidae
'... The spectacular evolutionary radiation of hummingbirds (Trochilidae) has served as a model system for many biological studies. To begin to provide a historical context for these investigations, we generated a complete matrix of DNA hybridization distances among 26 hummingbirds and an outgroup swift ...'
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The spectacular evolutionary radiation of hummingbirds (Trochilidae) has served as a model system for many biological studies. To begin to provide a historical context for these investigations, we generated a complete matrix of DNA hybridization distances among 26 hummingbirds and an outgroup swift (Chaeturu pelagica) to determine the principal hummingbird lineages. FITCH topologies estimated from symmetrized AT,H-C values and subjected to various validation methods (bootstrapping, weighted jackknifing, branch length significance) indicated a funda-mental split between hermit (Eutoxeres uquilu, Threnetes ruckeri; Phaethornithinae) and nonhermit (Trochilinae) hummingbirds, and provided strong support for six principal nonhermit clades with the following branching order: (1) a predominantly lowland group comprising caribs (Eulumpis holosericeus) and relatives (Androdon uequutoriulis
Molecular fingerprinting of hybrids and assessment of genetic purity of hybrid seeds in rice using microsatellite markers
'... Summary Microsatellite markers were used for fingerprinting of hybrids, assessing variation within parental lines and testing the genetic purity of hybrid seed lot in rice. Ten sequence tagged microsatellite sites (STMS) markers were employed for fingerprinting 11 rice hybrids and their parental li ...'
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Summary Microsatellite markers were used for fingerprinting of hybrids, assessing variation within parental lines and testing the genetic purity of hybrid seed lot in rice. Ten sequence tagged microsatellite sites (STMS) markers were employed for fingerprinting 11 rice hybrids and their parental lines. Nine STMS markers were found polymorphic across the hybrids and produced unique fingerprint for the 11 hybrids. A set of four markers (RM 206, RM 216, RM 258 and RM 263) differentiated all the hybrids from each other, which can be used as referral markers for unambiguous identification and protection of these hybrids. Cluster analysis based on Jaccard's similarity coefficient using UP-GMA grouped the hybrids into three clusters. Within the cluster all the hybrids shared a common cytoplasmic male sterile line as female parent. The genetic similarity between the hybrids ranged from 0.33 to 0.92 with an average similarity index of 0.63. The analysis of plant-to-plant variation within the parental lines of the hybrid Pusa RH 10, using informative markers indicated residual heterozygosity at two marker loci. This highlights the importance of STMS markers in maintaining the genetic purity of the parental lines. The unique value of the restorer gene linked marker for testing the genetic purity of hybrid seeds is demonstrated for the first time.
RAPD and ISSR molecular markers in Olea europaea L.: genetic variability and molecular cultivar identification. Genetic Resources and Crop Evolution 54
'... Thirty Portuguese and eight foreign olive (Olea europaea L.) cultivars were screened using Random Amplified Polymorphic DNA (RAPD) and Inter-Simple Sequence Repeat (ISSR) markers. Twenty RAPD primers amplified 301 reproducible bands of which 262 were polymorphic; and 17 ISSR primers amplified 204 ba ...'
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Thirty Portuguese and eight foreign olive (Olea europaea L.) cultivars were screened using Random Amplified Polymorphic DNA (RAPD) and Inter-Simple Sequence Repeat (ISSR) markers. Twenty RAPD primers amplified 301 reproducible bands of which 262 were polymorphic; and 17 ISSR primers amplified 204 bands of which 180 were polymorphic. The percentage of polymorphic bands detected by ISSR and RAPD was similar (88 and 87%, respectively). The genetic variability observed was similar in the Portu-guese and foreign olive cultivars. Seven ISSR and 12 RAPD primers were able to distinguish individually all 38 olive cultivars. Twenty specific molecular markers are now available to be converted into Sequence Characterised Amplified Region (SCAR) markers. Relationships among Portuguese and foreign cultivars is discussed.
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Trophic structure in a large assemblage of phyllostomid bats in
'... Bats of the family Phyllostomidae are fundamental components of Neotropical mammalian diversity and display the greatest dietary diversity seen in any mammalian family. We studied trophic structure in a species-rich local assemblage of phyllostomids for which dietary data were collected during 10 ye ...'
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Bats of the family Phyllostomidae are fundamental components of Neotropical mammalian diversity and display the greatest dietary diversity seen in any mammalian family. We studied trophic structure in a species-rich local assemblage of phyllostomids for which dietary data were collected during 10 years on Barro Colorado Island, Panama. Correspondence analysis of /3800 dietary records from 30 syntopic species showed a structure supporting traditional divisions of animalivorous and phytophagous phyllostomids. Putatively omnivorous species actually grouped among the latter. Phytophagous phyllostomids separated into Piper-specialists, Ficus-specialists, and eclectic plant eaters which in turn were the main consumers of flower products. Discrete dietary groups were compatible with several clades of the two current phylogenetic hypotheses of phyllostomids. We show that the trophic structure of the local contemporary assemblage is largely conservative with respect to traceable ancestral habits, strongly suggesting that overall trophic structure was likely determined historically.
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NCSS contains several tools for clustering, including K-Means clustering, fuzzy clustering, and medoid partitioning. Each procedure is easy to use and is validated for accuracy. Use the links below to jump to a clustering topic. To see how these tools can benefit you, we recommend you download and install the free trial of NCSS.
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Introduction
Clustering or cluster analysis is the process of grouping individuals or items with similar characteristics or similar variable measurements. Various algorithms and visualizations are available in NCSS to aid in the clustering process.
Technical Details
This page provides a general overview of the tools that are available in NCSS for a cluster statistical analysis. If you would like to examine the formulas and technical details relating to a specific NCSS procedure, click on the corresponding ‘[Documentation PDF]’ link under each heading to load the complete procedure documentation. There you will find formulas, references, discussions, and examples or tutorials describing the procedure in detail.
Hierarchical Clustering / Dendrograms
The agglomerative hierarchical clustering algorithms available in this procedure build a cluster hierarchy that is commonly displayed as a tree diagram called a dendrogram. The algorithms begin with each object in a separate cluster. At each step, the two clusters that are most similar are joined into a single new cluster. Once fused, objects are never separated. The eight methods that are available represent eight methods of defining the similarity between clusters.
The eight clustering techniques (linkage types) in this procedure are:
- Single Linkage: Also known as nearest neighbor clustering, this is one of the oldest and most famous of the hierarchical techniques. The distance between two groups is defined as the distance between their two closest members. It often yields clusters in which individuals are added sequentially to a single group.
- Complete Linkage: Also known as furthest neighbor or maximum method, this method defines the distance between two groups as the distance between their two farthest-apart members. This method usually yields clusters that are well separated and compact.
- Simple Average: Also called the weighted pair-group method, this algorithm defines the distance between groups as the average distance between each of the members, weighted so that the two groups have an equal influence on the final result.
- Centroid: Also referred to as the unweighted pair-group centroid method, this method defines the distance between two groups as the distance between their centroids (center of gravity or vector average). The method should only be used with Euclidean distances.
- Median: Also called the weighted pair-group centroid method, this defines the distance between two groups as the weighted distance between their centroids, the weight being proportional to the number of individuals in each group. The method should only be used with Euclidean distances.
- Group Average: Also called the unweighted pair-group method, this is perhaps the most widely used of all the hierarchical cluster techniques. The distance between two groups is defined as the average distance between each of their members.
- Ward’s Minimum Variance: With this method, groups are formed so that the pooled within-group sum of squares is minimized. That is, at each step, the two clusters are fused which result in the least increase in the pooled within-group sum of squares.
- Flexible Strategy: Lance and Williams suggested that a continuum could be made between single and complete linkage. The program also allows you to try various settings of these parameters which do not conform to the constraints suggested by Lance and Williams.
Euclidean or Manhattan distances may be used in these clustering techniques.
Example Dataset of Clustering Data
Example Setup of the Hierarchical Clustering / Dendrograms Procedure
Ntsys Cluster Analysis Meaning
Example Output for the Hierarchical Clustering / Dendrograms Procedure
A Dendrogram from the Hierarchical Clustering / Dendrograms Procedure
K-Means Clustering
The k-means algorithm was developed by J.A. Hartigan and M.A. Wong of Yale University as a partitioning technique. It is most useful for forming a small number of clusters from a large number of observations. It requires variables that are continuous with no outliers.
The objective of this technique is to divide N observations with P dimensions (variables) into K clusters so that the within-cluster sum of squares is minimized. Since the number of possible arrangements is enormous, it is not practical to expect the single best solution. Rather, this algorithm finds a “local” optimum. This is a solution in which no movement of an observation from one cluster to another will reduce the within-cluster sum of squares. The algorithm may be repeated several times with different starting configurations. The optimum of these cluster solutions is then selected.
Some of the reports available in the this procedure include iteration details, cluster means, F-Ratios, distance sections, and bivariate plots.
Some Bivariate Plots from the K-Means Clustering Procedure
Medoid Partitioning
The objective of cluster analysis is to partition a set of objects into two or more clusters such that objects within a cluster are similar and objects in different clusters are dissimilar. The medoid partitioning algorithms available in this procedure attempt to accomplish this by finding a set of representative objects called medoids. The medoid of a cluster is defined as that object for which the average dissimilarity to all other objects in the cluster is minimal. If k clusters are desired, k medoids are found. Once the medoids are found, the data are classified into the cluster of the nearest medoid.
Two algorithms are available in this procedure to perform the clustering. The first, from Spath, uses random starting cluster configurations. The second, from Kaufman and Rousseeuw, makes special use of silhouette statistics to help determine the appropriate number of clusters.
Medoid Algorithm of Spath
This method minimizes an objective function by swapping objects from one cluster to another. Beginning at a random starting configuration, the algorithm proceeds to a local minimum by intelligently moving objects from one cluster to another. When no object moving would result in a reduction of the objective function, the procedure terminates. Unfortunately, this local minimum is not necessarily the global minimum. To overcome this limitation, the program lets you rerun the algorithm using several random starting configurations and the best solution is kept.
Medoid Algorithm of Kaufman and Rousseeuw
Kaufman and Rousseeuw present a medoid algorithm which they call PAM (Partition Around Medoids). This algorithm also attempts to minimize the total distance D (formula given above) between objects within each cluster. The algorithm proceeds through two phases.
In the first phase, a representative set of k objects is found. The first object selected has the shortest distance to all other objects. That is, it is in the center. An addition k-1 objects are selected one at a time in such a manner that at each step, they decrease D as much as possible.
In the second phase, possible alternatives to the k objects selected in phase one are considered in an iterative manner. At each step, the algorithm searches the unselected objects for the one that if exchanged with one of the k selected objects will lower the objective function the most. The exchange is made and the step is repeated. These iterations continue until no exchanges can be found that will lower the objective function.
Note that all potential swaps are considered and that the algorithm does not depend on the order of the objects on the database.
Fuzzy Clustering
Fuzzy clustering generalizes partition clustering methods (such as k-means and medoid) by allowing an individual to be partially classified into more than one cluster. In regular clustering, each individual is a member of only one cluster. Suppose we have K clusters and we define a set of variables that represent the probability that object i is classified into cluster k. In partition clustering algorithms, one of these values will be one and the rest will be zero. This represents the fact that these algorithms classify an individual into one and only one cluster.
In fuzzy clustering, the membership is spread among all clusters. The probability of each object to be in each cluster can now be between zero and one, with the stipulation that the sum of their values is one. We call this a fuzzification of the cluster configuration. It has the advantage that it does not force every object into a specific cluster. It has the disadvantage that there is much more information to be interpreted.
Regression Clustering
The algorithm used in this procedure provides for clustering in the multiple regression setting in which you have a dependent variable Y and one or more independent variables, the X’s. The algorithm partitions the data into two or more clusters and performs an individual multiple regression on the data within each cluster. It is based on an exchange algorithm described in Spath.
Regression Exchange Algorithm
This algorithm is fairly simple to describe. The number of clusters, K, for a given run is fixed. The rows are randomly sorted into the groups to form K initial clusters. An exchange algorithm is applied to this initial configuration which searches for the rows of data that would produce a maximum decrease in a least-squares penalty function (that is, maximizing the increase in R-squared at each step). The algorithm continues until no beneficial exchange of rows can be found.
The following chart shows data that were clustered using this algorithm. Notice how the two clusters actually intersect.
Output from the NCSS Scatter Plot Procedure based on Regression Clustering Procedure Results