Algorithms for clustering data
Algorithms for clustering data
The art of computer programming, volume 2 (3rd ed.): seminumerical algorithms
The art of computer programming, volume 2 (3rd ed.): seminumerical algorithms
Genetic Algorithms and Grouping Problems
Genetic Algorithms and Grouping Problems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
An evolutionary technique based on K-means algorithm for optimal clustering in RN
Information Sciences—Applications: An International Journal
Spatial Clustering for Data Mining with Genetic Algorithms
Spatial Clustering for Data Mining with Genetic Algorithms
Combining Multiple Clusterings Using Evidence Accumulation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Pattern Recognition, Third Edition
Pattern Recognition, Third Edition
Top 10 algorithms in data mining
Knowledge and Information Systems
On the efficiency of evolutionary fuzzy clustering
Journal of Heuristics
Cluster Analysis
A survey of evolutionary algorithms for clustering
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Comparison Among Methods for k Estimation in k-means
ISDA '09 Proceedings of the 2009 Ninth International Conference on Intelligent Systems Design and Applications
Fast Evolutionary Algorithms for Relational Clustering
ISDA '09 Proceedings of the 2009 Ninth International Conference on Intelligent Systems Design and Applications
Relative clustering validity criteria: A comparative overview
Statistical Analysis and Data Mining
Efficiency issues of evolutionary k-means
Applied Soft Computing
Robust fuzzy clustering of relational data
IEEE Transactions on Fuzzy Systems
On cluster validity for the fuzzy c-means model
IEEE Transactions on Fuzzy Systems
Evolutionary fuzzy clustering of relational data
Theoretical Computer Science
Automatic aspect discrimination in data clustering
Pattern Recognition
Evolutionary k-means for distributed data sets
Neurocomputing
Binarization based edge detection using universal law of gravity and ant colony optimization
International Journal of Hybrid Intelligent Systems
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This paper is concerned with the computational efficiency of clustering algorithms when the data set to be clustered is described by a proximity matrix only (relational data) and the number of clusters must be automatically estimated from such data. Two relational versions of an evolutionary algorithm for clustering are derived and compared against two systematic (pseudo-exhaustive) approaches that can also be used to automatically estimate the number of clusters in relational data. The computational complexities of the algorithms are discussed and an extensive collection of experiments involving 18 artificial and two real data sets is reported and analyzed.