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Measure of how connected and clustered a node is in its graph
the clustering in the network, whereas the local gives an indication of the extent of "clustering" of a single node. The local clustering coefficient of
Clustering_coefficient
Method of generating random small-world graphs
probability of two nodes being connected, ER graphs have a low clustering coefficient. They do not account for the formation of hubs. Formally, the degree
Watts–Strogatz_model
Graph where most nodes are reachable in a small number of steps
graph characterized by a high clustering coefficient and low distances. In an example of a social network, high clustering implies the high probability
Small-world_network
Scale-free network generation algorithm
trivial: networks are trees and the clustering coefficient is equal to zero. An analytical result for the clustering coefficient of the BA model was obtained
Barabási–Albert_model
Quality measure in cluster analysis
have a low or negative value, then the clustering configuration may have too many or too few clusters. A clustering with an average silhouette width of over
Silhouette_(clustering)
the distribution of the nodes' clustering coefficients: as other models would predict a constant clustering coefficient as a function of the degree of
Hierarchical_network_model
Concept in social network theory
order) the clustering coefficient and transitivity for that graph. One measure for the presence of triadic closure is clustering coefficient, as follows:
Triadic_closure
Type of clustering of data points
clustering (also referred to as soft clustering or soft k-means) is a form of clustering in which each data point can belong to more than one cluster
Fuzzy_clustering
Topics referred to by the same term
formation of clusters of linked nodes in a network, measured by the clustering coefficient Correlation clustering, a way of clustering nodes in a signed
Clustering
Academic field
The clustering coefficient for the entire network is the average of the clustering coefficients of all the nodes. A high clustering coefficient for a
Network_science
Family of random graph models
above, the global clustering coefficient is an inverse function of the network size, so for large configuration networks, clustering tends to be small
Configuration_model
Network with non-trivial topological features
features include a heavy tail in the degree distribution, a high clustering coefficient, assortativity or disassortativity among vertices, community structure
Complex_network
Network whose degree distribution follows a power law
Another important characteristic of scale-free networks is the clustering coefficient distribution, which decreases as the node degree increases. This
Scale-free_network
Grouping a set of objects by similarity
statistical distributions. Clustering can therefore be formulated as a multi-objective optimization problem. The appropriate clustering algorithm and parameter
Cluster_analysis
Vector quantization algorithm minimizing the sum of squared deviations
k-means clustering is a method of vector quantization, originally from signal processing, that aims to partition n observations into k clusters in which
K-means_clustering
Statistical method in data analysis
clusters. Strategies for hierarchical clustering generally fall into two categories: Agglomerative: Agglomerative clustering, often referred to as a "bottom-up"
Hierarchical_clustering
Social structure made up of a set of social actors
context. Another general characteristic of scale-free networks is the clustering coefficient distribution, which decreases as the node degree increases. This
Social_network
Measure of linear correlation
statistics, the Pearson correlation coefficient (PCC), also known as Pearson's r, the Pearson product-moment correlation coefficient (PPMCC), or simply the unqualified
Pearson correlation coefficient
Pearson_correlation_coefficient
Metric of clustering solutions quality
Density-Based Clustering Validation (DBCV) is a metric designed to assess the quality of clustering solutions, particularly for density-based clustering algorithms
Density-based clustering validation
Density-based_clustering_validation
Numerical measure of a statistical relationship between variables
A correlation coefficient is a numerical measure of some type of linear correlation, meaning a linear function between two variables. The variables may
Correlation_coefficient
Analysis of social structures using network and graph theory
is wanted. Clustering coefficient: A measure of the likelihood that two associates of a node are associates. A higher clustering coefficient indicates
Social_network_analysis
In graph theory, the mathematically simplest spatial network
Hamiltonian cycle. The clustering coefficient of RGGs only depends on the dimension d of the underlying space [0,1)d. The clustering coefficient is C d = 1 − H
Random_geometric_graph
vertices in a directed network to be mutually linked. Like the clustering coefficient, scale-free degree distribution, or community structure, reciprocity
Reciprocity_(network_science)
Relative measure of dispersion expressed as the ratio of standard deviation to the mean
In probability theory and statistics, the coefficient of variation (CV), also known as normalized root-mean-square deviation (NRMSD), and relative standard
Coefficient_of_variation
Descriptive statistic
statistics, the intraclass correlation, or the intraclass correlation coefficient (ICC), is a descriptive statistic that can be used when quantitative
Intraclass_correlation
Theory on how and why new ideas spread
firms that influence their decision to innovate are clustering, weak ties, and firm size. Clustering, the existence of a group of tightly connected agents
Diffusion_of_innovations
Measure of a graph's centrality, based on shortest paths
local clustering coefficient-dependent degree centrality (LCCDC), use only local structural properties such as degree and the clustering coefficient to estimate
Betweenness_centrality
Probability distribution
referred to as the "dispersion parameter", "shape parameter" or "clustering coefficient", or the "heterogeneity" or "aggregation" parameter. The term "aggregation"
Negative binomial distribution
Negative_binomial_distribution
Similarity measure for number sequences
Other names for cosine similarity include Orchini similarity and Tucker coefficient of congruence; the Otsuka–Ochiai similarity (see below) is cosine similarity
Cosine_similarity
Measure of similarity and diversity between sets
an n × n matrix for clustering and multidimensional scaling of n sample sets. These distance measures are commonly used in cluster analysis for grouping
Jaccard_index
Concept in network topology
three most robust measures of network topology, along with its clustering coefficient and its degree distribution. Some examples are: the average number
Average_path_length
Graph generated by a random process
distribution, but with degree correlations and a significantly higher clustering coefficient. Given a random graph G of order n with the vertex V(G) = {1, .
Random_graph
Overview of and topical guide to machine learning
Hierarchical clustering Single-linkage clustering Conceptual clustering Cluster analysis BIRCH DBSCAN Expectation–maximization (EM) Fuzzy clustering Hierarchical
Outline_of_machine_learning
Two closely related models for generating random graphs
distribution, but with degree correlations and a significantly higher clustering coefficient. In percolation theory one examines a finite or infinite graph and
Erdős–Rényi_model
useful in evaluating clustering algorithms since cluster labels typically have no particular ordering. The uncertainty coefficient is not symmetric with
Uncertainty_coefficient
Approach to understanding the human brain
occasionally experience random activity. In small-world networks, the clustering coefficient (i.e., transitivity) is high, and the average path distance is short
Network_neuroscience
Nonparametric measure of rank correlation
In statistics, Spearman's rank correlation coefficient or Spearman's ρ is a number ranging from -1 to 1 that indicates how strongly two sets of ranks
Spearman's rank correlation coefficient
Spearman's_rank_correlation_coefficient
Expansion coefficients in statistical mechanics
Virial coefficients B i {\displaystyle B_{i}} appear as coefficients in the virial expansion of the pressure of a many-particle system in powers of the
Virial_coefficient
Statistic for rank correlation
In statistics, the Kendall rank correlation coefficient, commonly referred to as Kendall's τ coefficient (after the Greek letter τ, tau), is a statistic
Kendall rank correlation coefficient
Kendall_rank_correlation_coefficient
Measure of network community structure
communities may have quite different properties such as node degree, clustering coefficient, betweenness, centrality, etc., from that of the average network
Modularity_(networks)
Subgraph induced by all nodes linked to a given node of a graph
adjacency matrix representations. Neighbourhoods are also used in the clustering coefficient of a graph, which is a measure of the average density of its neighbourhoods
Neighbourhood_(graph_theory)
Statistic used for comparing the similarity of two samples
The Dice-Sørensen coefficient is a statistic used to gauge the similarity of two samples. It was independently developed by the botanists Lee Raymond
Dice-Sørensen_coefficient
respectively. (Guimera et al.) The shipping network is highly clustered, its clustering coefficient is 0.49, which can be interpreted that any given node's
Global_shipping_network
Mathematical concept
also offers a context-free measure of connectedness, called the clustering coefficient. Other fields of mathematics are concerned with objects that are
Connectedness
Networks with multiple kinds of relations
}u^{\alpha }} Like many other network statistics, the meaning of a clustering coefficient becomes ambiguous in multidimensional networks, due to the fact
Multidimensional_network
flows in an online payment system exhibit free-scale property, high clustering coefficient, and small world phenomenon and that after the September 11 attacks
Economics_of_networks
Density-based data clustering algorithm
Density-based spatial clustering of applications with noise (DBSCAN) is a data clustering algorithm proposed by Martin Ester, Hans-Peter Kriegel, Jörg
DBSCAN
Statistical modeling method
explanatory variable with a slope coefficient. A multiple regression e right hand side, each with its own slope coefficient Rencher, Alvin C.; Christensen
Linear_regression
Cluster analysis problem
issue from the process of actually solving the clustering problem. For a certain class of clustering algorithms (in particular k-means, k-medoids and
Determining the number of clusters in a data set
Determining_the_number_of_clusters_in_a_data_set
Network where the ties among nodes have weights assigned to them
Dijkstra's distance algorithm The clustering coefficient (global): Redefined by using a triplet value The clustering coefficient (local): Redefined by using
Weighted_network
Property of graphs that depends only on abstract structure
Hosoya index Wiener index Colin de Verdière graph invariant Boxicity Clustering coefficient Betweenness centrality Fractional chromatic number Algebraic connectivity
Graph_property
Similarity of two probability distributions
probability distributions. It is closely related to the Bhattacharyya coefficient, which is a measure of the amount of overlap between two statistical
Bhattacharyya_distance
Statistical measure of association for two binary variables
In statistics, the phi coefficient, also known as the mean square contingency coefficient or Yule coefficient of correlation and commonly denoted by φ
Phi_coefficient
possible to get analytic results about the degree distribution, clustering coefficient, average shortest path length, random walk centrality and other
Deterministic scale-free network
Deterministic_scale-free_network
Metric for evaluating clustering algorithms
of clusters, a higher Dunn index indicates better clustering. One of the drawbacks of using this is the computational cost as the number of clusters and
Dunn_index
Iron Age culture in central Italy
from Osteria, there is a grave situated within the center of a funerary cluster that belongs to an older male who was entombed with a life-sized—not miniature—weapon
Latial_culture
Measure of similarity between two data clusterings
expected by chance. It is commonly used to compare a clustering algorithm's output with a reference clustering, such as known class labels or a ground-truth
Rand_index
Size: 225 226 Average degree: 61 Average path length: 3.65 Average clustering coefficient: 0.79 Compared to a random graph of the same size and average degree
Co-stardom_network
Iterative method for finding maximum likelihood estimates in statistical models
sensor data (see Operational Modal Analysis). EM is also used for data clustering. In natural language processing, two prominent instances of the algorithm
Expectation–maximization algorithm
Expectation–maximization_algorithm
distribution, variations in the degree exponent, or the size independent clustering coefficient. Therefore, the original model has since been modified[by whom?]
Evolving_network
Routing methods for networks with short node paths
for decentralized search. Kleinberg has shown that the optimal clustering coefficient for this model is q = 2 {\displaystyle q=2} , or an inverse square
Small-world_routing
Concept in operations management & industrial engineering
product-machines n-by-m matrix b i p {\displaystyle b_{ip}} , rank order clustering is an algorithm characterized by the following steps: For each row i compute
Production_flow_analysis
Graph measuring gene relationships
approaches have also been used such as threshold selection based on clustering coefficient or random matrix theory. The problem with p-value based methods
Gene_co-expression_network
Data mining technique for simultaneous clustering of the rows and columns of a matrix
Biclustering, block clustering, co-clustering or two-mode clustering is a data mining technique which allows simultaneous clustering of the rows and columns
Biclustering
Statistical measure of a dendrogram's faithfulness to the data
data tend to occur in clumps, or clusters. This coefficient has also been proposed for use as a test for nested clusters. Suppose that the original data
Cophenetic_correlation
redirects to k-means clustering K-means++ K-medians clustering K-medoids K-statistic Kalman filter Kaplan–Meier estimator Kappa coefficient Kappa statistic
List_of_statistics_articles
Network analysis and visualization package for Microsoft Excel
contains a library of commonly used graph metrics: centrality, clustering coefficient, and diameter. NodeXL differentiates between directed and undirected
NodeXL
percentages. Other measures include: Team clustering coefficient - A direct application of a clustering coefficient. It measures how interconnected are the
Network Science Based Basketball Analytics
Network_Science_Based_Basketball_Analytics
Clustering using tree-based data aggregation
iterative reducing and clustering using hierarchies) is an unsupervised data mining algorithm used to perform hierarchical clustering over particularly large
BIRCH
Type of inference
analysis, and clustering analysis. The transitivity or clustering coefficient of a network is a measure of the tendency of the nodes to cluster together.
Biological_network_inference
Statistical relationship
product-moment correlation coefficient, most commonly called 'Pearson's correlation coefficient' or simply 'the correlation coefficient' (as it is the most common
Correlation
Statistic measuring inter-rater agreement for categorical items
Cohen's kappa coefficient (symbol κ, lowercase Greek kappa) is a statistic used to measure inter-rater reliability for qualitative or categorical data
Cohen's_kappa
Method used to normalize the range of independent variables
similarities between data points, such as clustering and similarity search. As an example, the K-means clustering algorithm is sensitive to feature scales
Feature_scaling
distribution such as the average path length, spectral properties, and clustering coefficient, cannot be obtained from this mapping. The Simon model is related
Simon_model
Python module
flow, etc. Support for several centrality measures. Support for clustering coefficients, as well as network motif statistics and community structure detection
Graph-tool
Range to estimate an unknown parameter
for the parameter θ {\displaystyle \theta } , with confidence level or coefficient γ {\displaystyle \gamma } , is an interval ( u ( X ) , v ( X ) ) {\displaystyle
Confidence_interval
Extracting features from raw data for machine learning
decomposition has been extensively used for data clustering under non-negativity constraints on the feature coefficients. These include Non-Negative Matrix Factorization
Feature_engineering
Algorithms for matrix decomposition
equivalent to the minimization of K-means clustering. Furthermore, the computed H {\displaystyle H} gives the cluster membership, i.e., if H k j > H i j {\displaystyle
Non-negative matrix factorization
Non-negative_matrix_factorization
communication efficiencies can be used as an alternative to the clustering coefficient of a network. The local efficiency of a network G {\displaystyle
Efficiency_(network_science)
Measure of dependence between two variables
hierarchical clustering of sequences without having any domain knowledge of the sequences (Cilibrasi & Vitányi 2005). Unlike correlation coefficients, such as
Mutual_information
Table that displays the frequency of variables
applicable only to the case of 2 × 2 contingency tables, is the phi coefficient (φ) defined by ϕ = ± χ 2 N , {\displaystyle \phi =\pm {\sqrt {\frac {\chi
Contingency_table
Algorithm for labeling clusters on a grid
K-means clustering algorithm Fuzzy clustering algorithm Gaussian (Expectation Maximization) clustering algorithm Clustering Methods C-means Clustering Algorithm
Hoshen–Kopelman_algorithm
Graph formed by subdivision of triangles
Gang; Wang, Bing-Hong (2005), "Maximal planar networks with large clustering coefficient and power-law degree distribution", Physical Review E, 71 (4) 046141
Apollonian_network
Real-valued function that quantifies similarity between two objects
Euclidean distance, which is used in many clustering techniques including K-means clustering and Hierarchical clustering. The Euclidean distance is a measure
Similarity_measure
Metric that measures the distance between two strings of text
coefficient (SMC) Jaccard similarity or Jaccard coefficient or Tanimoto coefficient Tversky index Overlap coefficient Variational distance Hellinger distance
String_metric
Dictionary learning algorithm
value decomposition approach. k-SVD is a generalization of the k-means clustering method, and it works by iteratively alternating between sparse coding
K-SVD
Data clustering algorithm
(Clustering Using REpresentatives) is an efficient data clustering algorithm for large databases[citation needed]. Compared with K-means clustering it
CURE_algorithm
Type of feedforward neural network
machine learning Problems Classification Generative modeling Regression Clustering Dimensionality reduction Density estimation Anomaly detection Data cleaning
Multilayer_perceptron
Objects maximally similar to other objects in a dataset
standard k-medoids algorithm Hierarchical Clustering Around Medoids (HACAM), which uses medoids in hierarchical clustering From the definition above, it is clear
Medoid
Sequence of data points over time
series data may be clustered, however special care has to be taken when considering subsequence clustering. Time series clustering may be split into whole
Time_series
Branch of statistics mathematics
as the cluster centers. Covariance structures have also been taken into consideration. Besides k-means type clustering, functional clustering based on
Functional_data_analysis
Measure of the asymmetry of random variables
is sometimes referred to as Pearson's moment coefficient of skewness, or simply the moment coefficient of skewness, but should not be confused with Pearson's
Skewness
Type of large language model
machine learning Problems Classification Generative modeling Regression Clustering Dimensionality reduction Density estimation Anomaly detection Data cleaning
Generative pre-trained transformer
Generative_pre-trained_transformer
Machine learning technique
machine learning Problems Classification Generative modeling Regression Clustering Dimensionality reduction Density estimation Anomaly detection Data cleaning
Reinforcement learning from human feedback
Reinforcement_learning_from_human_feedback
Evaluation method in cluster analysis
that is used to determine the similarity between two clusterings (clusters obtained after a clustering algorithm), and also a metric to measure confusion
Fowlkes–Mallows_index
Meta-algorithmic technique to choose an algorithm
homogeneous clusters via an unsupervised clustering approach and associating an algorithm with each cluster. A new instance is assigned to a cluster and the
Algorithm_selection
Set of methods for supervised statistical learning
which attempt to find natural clustering of the data into groups, and then to map new data according to these clusters. The popularity of SVMs is likely
Support_vector_machine
Statistic comparing ordinal rankings
A rank correlation coefficient can measure that relationship, and the measure of significance of the rank correlation coefficient can show whether the
Rank_correlation
Statistical transformation
z-transformation) of a Pearson correlation coefficient is its inverse hyperbolic tangent (artanh). When the sample correlation coefficient r is near 1 or -1, its distribution
Fisher_transformation
Normalized measure of the dispersion of a probability distribution
dispersion, dispersion index, coefficient of dispersion, relative variance, or variance-to-mean ratio (VMR), like the coefficient of variation, is a normalized
Index_of_dispersion
CLUSTERING COEFFICIENT
CLUSTERING COEFFICIENT
Girl/Female
Hindu, Indian, Marathi, Sanskrit
Fearless; Fluttering
Surname or Lastname
English (chiefly Nottinghamshire)
English (chiefly Nottinghamshire) : nickname from the personal name Herod (Greek HÄ“rÅdÄ“s, apparently derived from hÄ“rÅs ‘hero’), borne by the king of Judea (died ad 4) who at the time of the birth of Christ ordered that all male children in Bethlehem should be slaughtered (Matthew 2: 16–18). In medieval mystery plays Herod was portrayed as a blustering tyrant, and the name was therefore given to someone one who had played the part, or who had an overbearing temper.English : variant of Harold (1 or 2).Greek : shortened form of Herodiadis, a patronymic from the classical personal name HÄ“rodiÅn. This was the name of a relative of St. Paul and an early Bishop of Patras, venerated in the Orthodox Church. HÄ“rodÄ“s ‘Herod’ is also found in Greek as a nickname for a violent man, but this is less likely to be the source of the surname.
CLUSTERING COEFFICIENT
CLUSTERING COEFFICIENT
Girl/Female
Hebrew American Italian Shakespearean Spanish
Devoted to God.
Boy/Male
Tamil
Emperor
Girl/Female
English
Feminine.
Boy/Male
Christian & English(British/American/Australian)
Valiant
Girl/Female
Indian
Pleasant, Agreeable
Boy/Male
Gujarati, Hindu, Indian, Kannada, Sanskrit, Telugu
Compassionate
Girl/Female
Irish American
Present. Also a feminine form of Sean: Irish God is gracious; gift from God.
Girl/Female
Australian, Christian, French, Latin
Rising; Dawning; Golden
Girl/Female
Basque Spanish
Girl/Female
Australian, Vietnamese
A Type of Flower
CLUSTERING COEFFICIENT
CLUSTERING COEFFICIENT
CLUSTERING COEFFICIENT
CLUSTERING COEFFICIENT
CLUSTERING COEFFICIENT
adv.
With clattering.
n.
A blustering noise; a swaggering behavior.
a.
Blustering; violent.
a.
Inclined to bluster; given to blustering; blustering.
a.
Exhibiting noisy violence, as the wind; stormy; tumultuous.
a.
Blustering; swaggering.
a.
Resembling a hector; blustering; insolent; taunting.
a.
Unstable; fluttering.
adv.
In a fluttering manner.
p. pr. & vb. n.
of Clutter
a.
Jovial and blustering; dashing.
a.
Uttering noisy threats; noisy and swaggering; boisterous.
a.
Disposed to be blustering or arrogant; petulant.
adv.
In a glistering manner.
n.
A blustering, talkative fellow.
p. pr. & vb. n.
of Fluster
p. pr. & vb. n.
of Cluster
n.
A blustering, turbulent fellow.
adv.
In a blustering manner.
p. pr. & vb. n.
of Bluster