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Cluster analysis

Cluster analysis, or clustering, is a data analysis technique aimed at partitioning a set of objects into groups such that objects within the same group (called a cluster) exhibit greater similarity to one another (in some specific sense defined by the analyst) than to those in other groups (clusters). It is a main task of exploratory data analysis, and…

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Definition

Hierarchical clustering K-means algorithm Multivariate normal distributions Expectation-maximization algorithm DBSCAN

22 related topics

Algorithms

Dendrogram Single-linkage clustering Complete linkage clustering UPGMA WPGMA

44 related topics

Applications

Natural sciences Dimensionality reduction Principal component analysis T-SNE Search engines

13 related topics

Overview

Similarity Exploratory data analysis Statistical Data analysis Pattern recognition

130 related topics

Topics to explore

Browse the full topic structure. Each item opens a new analysis centered on that subject.

Overview

Similarity Similarity measure
Exploratory data analysis
Statistical Statistics
Data analysis
Pattern recognition
Image analysis
Information retrieval
Bioinformatics
Data compression
Computer graphics
Machine learning
Algorithm
Distances Distance function
Statistical distributions Statistical distribution
Multi-objective optimization
Distance function Metric (mathematics)
Data set
Knowledge discovery
Data preprocessing
Classification Statistical classification
Numerical taxonomy
Greek Greek language
Communities Community structure
Joseph Zubin
Robert Tryon
Cattell Raymond Cattell
Personality psychology
Davies–Bouldin index
Centroid
Dunn index

Definition

Hierarchical clustering
K-means algorithm
Multivariate normal distributions Multivariate normal distribution
Expectation-maximization algorithm
DBSCAN
OPTICS
HDBSCAN
Biclustering
Clique Clique (graph theory)
Graph Graph (discrete mathematics)
Path Path (graph theory)
Signed graph
Sign Sign (mathematics)
Balance theory
Cycle Cycle (graph theory)
Unsupervised Unsupervised learning
Neural network
Self-organizing map
Principal Component Analysis
Independent Component Analysis
Fuzzy clustering
Subspace clustering

Algorithms

Dendrogram
Single-linkage clustering
Complete linkage clustering
UPGMA
WPGMA
Divisive clustering
K-means clustering
NP-hard
Lloyd's algorithm
Local optimum
K-medoids
Medians Median
K-medians clustering
K-means++
Number of clusters Determining the number of clusters in a data set
Euclidean distance
Voronoi diagram
Facility location problem Optimal facility location
Model-based clustering
Distribution models Probability distribution
Overfitting
Eigenvalue decomposition
Gaussian distributions Gaussian distribution
Correlation and dependence
Deterministic Deterministic algorithm
OPTICS OPTICS algorithm
R-tree
EM clustering Expectation–maximization algorithm
Mean-shift
Kernel density estimation

Evaluation and assessment

Gold standard Gold standard (test)
Constrained clustering
Hopkins statistic
Multimodality Multimodal distribution

Ethics and Fairness

Applications

Natural sciences Natural science
Dimensionality reduction
Principal component analysis
T-SNE T-distributed stochastic neighbor embedding
Search engines Search engine
Spectral clustering
Feature extraction
Embedding Word embedding
Vector space
Distance based Distance matrix
Business
Economics
Social Sciences Social science

Specialized types of cluster analysis

Techniques used in cluster analysis

Artificial neural network
Nearest neighbor search
Neighbourhood components analysis
Latent class analysis Latent class model
Affinity propagation

Data projection and preprocessing

Dimension reduction

Other

Cluster-weighted modeling
Parallel coordinates
Structured data analysis Structured data analysis (statistics)
Linear separability

Advanced semantic analysis

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Map overview Semantic statistics

Cluster analysis

Nodes244

Edges243

Triples37

Avg. degree1.99

Density0.008197

Components1

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Cluster analysis

Top relations

has application · 1

Cluster analysis → Cluster

Important terminology Word statistics

Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.

Important terminology

clustering data clusters cluster used algorithm analysis algorithms set k-means based number index similar one evaluation distance results different models

Entity relationships Subject–Predicate–Object triples

Subject	Predicate	Object	Confidence	Src
the distance function to use	instance of	including parameters	0.80	text
a density threshold or the number of expected clusters	instance of	including parameters	0.80	text
k-means	instance of	optimal centroids and assignmentsCentroid-based clustering problems	0.80	text
k-medoids are special cases of the uncapacitated	instance of	optimal centroids and assignmentsCentroid-based clustering problems	0.80	text
metric facility location problem	instance of	optimal centroids and assignmentsCentroid-based clustering problems	0.80	text
a canonical problem in the operations research	instance of	optimal centroids and assignmentsCentroid-based clustering problems	0.80	text
computational geometry communities	instance of	optimal centroids and assignmentsCentroid-based clustering problems	0.80	text
how many clusters there are	instance of	It has the advantages of providing principled statistical answers to questions	0.80	text
what clustering method or model to use	instance of	It has the advantages of providing principled statistical answers to questions	0.80	text
and how to detect	instance of	It has the advantages of providing principled statistical answers to questions	0.80	text
deal with outliers.While the theoretical foundation of these methods is excellent	instance of	It has the advantages of providing principled statistical answers to questions	0.80	text
they suffer from overfitting unless constraints are put on the model complexity	instance of	It has the advantages of providing principled statistical answers to questions	0.80	text

Related concept clusters Concept neighborhoods

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