Genetic algorithm-based clustering technique
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Summary
The superiority of the GA-clustering algorithm over the commonly used K-means algorithm is extensively demonstrated for four artificial and three real-life data sets.
- Type
- article
- Published
- 2000-09-01
- Cited by
- 1,489
- References
- 31
- OpenAlex
- https://openalex.org/W2071965987
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:684904
Keywords
Cluster analysis, Computer science, Determining the number of clusters in a data set, Genetic algorithm, CURE data clustering algorithm
References
- Pattern recognition
- Pattern recognition : a statistical approach
- A Graph-Theoretic Approach to Nonparametric Cluster Analysis
- Genetic algorithms and neural networks: optimizing connections and connectivity
- Algorithms for Clustering Data
- Pattern classification with genetic algorithms
- THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS
- Editorial Special issue on genetic algorithms
- Genetic algorithms for optimal image enhancement
- Genetic Algorithm with Elitist Model and Its Convergence
- Selection of Optimal Set of Weights in a Layered Network Using Genetic Algorithms
- Clustering techniques: The user's dilemma
- A Branch and Bound Clustering Algorithm
- K-Means-Type Algorithms: A Generalized Convergence Theorem and Characterization of Local Optimality
- Constrained Optimization Via Genetic Algorithms
- PATTERN CLUSTERING BY MULTIVARIATE MIXTURE ANALYSIS.
- Cluster Analysis for Applications
- Genetic Algorithms + Data Structures = Evolution Programs
- Genetic Algorithms in Search
- Genetic-algorithm programming environments
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- GENETIC ALGORITHMS AND CROSS-CORRELATION CLUSTERING OF TIME SERIES
- Medical Image Segmentation Using Genetic Algorithms
- Fuzzy clustering in the analysis of Fourier transform infrared spectra for cancer diagnosis.
- Recent developments in clustering algorithms
- Traitement de la mission et des variables environnementales et intégration au processus de conception systémique
- Towards hierarchical clustering
- A Review of Applications of Evolutionary Algorithms in Pattern Recognition
- Energy Efficient Communication Protocols for Wireless Sensor Networks
- Spherical conditions with XCSc
- Study On Clustering Techniques And Application To Microarray Gene Expression Bioinformatics Data
- Using genetic algorithms in K-means fast learning artificial neural networks for clustering.
- A Fast k-means Algorithm using Cluster Shifting to Produce Compact and Separate Clusters (RESEARCH NOTE)
- Improved Crisp and Fuzzy Clustering Techniques for Categorical Data
- Pattern Clustering using Soft-Computing Approaches
- Clustering Using Annealing Evolution: Application to Pixel Classification of Satellite Images
- Region-based crossover for clustering problems
- Data Mining in Market Segmentation: A Literature Review and Suggestions
- Genetic Algorithms Applied to Multi-Class Clustering for Gene Expression Data
- Tehran driving cycle development using the k-means clustering method
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