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Everything about Automatic clustering algorithms
Data processing algorithm
Automatic clustering algorithms are algorithms that can perform clustering without prior knowledge of data sets. In contrast with other clustering techniques, automatic clustering algorithms can determine the optimal number of clusters even in the presence of noise and outliers. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Automatic clustering algorithmsA 5-minute orientation
- Skim a recent paper: A Review of Quantum-Inspired Metaheuristic Algorithms for Automatic ClusteringInfluential work since 2022
- Pick up a classic book: Fuzzy Models and Algorithms for Pattern Recognition and Image ProcessingThe most-reprinted book on the subject
- Read the landmark paper: Automatic clustering algorithms: a systematic review and bibliometric analysis of relevant literatureCited 144 times
- Go sideways: DBSCANA closely related topic
- Go sideways: Cluster analysisA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
Automatic clustering algorithms: a systematic review and bibliometric analysis of relevant literature
Automatic clustering using genetic algorithms
A Comparative Performance Study of Hybrid Firefly Algorithms for Automatic Data Clustering
A comparison of clustering algorithms for automatic modulation classification
New automatic fuzzy relational clustering algorithms using multi-objective NSGA-II
Machine learning clustering algorithms for the automatic generation of chemical reactor networks from CFD simulations
Automatic Data Clustering by Hybrid Enhanced Firefly and Particle Swarm Optimization Algorithms
Clustering Algorithms in an Educational Context: An Automatic Comparative Approach
🆕 Influential since 2022
A Review of Quantum-Inspired Metaheuristic Algorithms for Automatic Clustering
An Automatic Fuzzy Clustering Algorithm for Discrete Elements
Automatic clustering of colour images using quantum inspired meta-heuristic algorithms
Clustering-Guided Automatic Generation of Algorithms for the Multidimensional Knapsack Problem
Automatic Data Clustering using Dynamic Crow Search Algorithm
📚 Books worth reading
Fuzzy Models and Algorithms for Pattern Recognition and Image ProcessingJames C. Bezdek · 2005
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
- DBSCAN Density-based spatial clustering of applications with noise (DBSCAN) is a data clustering algorithm proposed by Martin Ester, Hans-Peter Kriegel, Jörg…
- 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…
- OPTICS algorithm Ordering points to identify the clustering structure (OPTICS) is an algorithm for finding density-based clusters in spatial data. It was presented in 1999…
- BIRCH BIRCH (balanced iterative reducing and clustering using hierarchies) is an unsupervised data mining algorithm used to perform hierarchical clustering over…
- ELKI ELKI (Environment for Developing KDD-Applications Supported by Index-Structures) is a data mining (KDD, knowledge discovery in databases) software framework…
- Local outlier factor In anomaly detection, the local outlier factor (LOF) is an algorithm proposed by Markus M. Breunig, Hans-Peter Kriegel, Raymond T. Ng and Jörg Sander in…
- Hierarchical clustering In data mining and statistics, hierarchical clustering (also called hierarchical cluster analysis or HCA) is a method of cluster analysis that seeks to…
- K-medoids The k-medoids method is used in unsupervised learning. It is a classical partitioning technique of clustering that splits a data set of n objects into…