Simulated annealing: theory and applications
Simulated annealing: theory and applications
A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Modified Version of the K-Means Algorithm with a Distance Based on Cluster Symmetry
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Performance Evaluation of Some Clustering Algorithms and Validity Indices
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stability-based validation of clustering solutions
Neural Computation
Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art
Evolutionary Computation
Classification and Learning Using Genetic Algorithms: Applications in Bioinformatics and Web Intelligence (Natural Computing Series)
GAPS: A clustering method using a new point symmetry-based distance measure
Pattern Recognition
A genetic algorithm for cluster analysis
Intelligent Data Analysis
Top 10 algorithms in data mining
Knowledge and Information Systems
Fast and effective clustering of XML data using structural information
Knowledge and Information Systems
Knowledge and Information Systems
Clustering multidimensional sequences in spatial and temporal databases
Knowledge and Information Systems
A Point Symmetry-Based Clustering Technique for Automatic Evolution of Clusters
IEEE Transactions on Knowledge and Data Engineering
Clustering based on matrix approximation: a unifying view
Knowledge and Information Systems
Pattern-based time-series subsequence clustering using radial distribution functions
Knowledge and Information Systems
Nonparametric genetic clustering: comparison of validity indices
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
An Evolutionary Approach to Multiobjective Clustering
IEEE Transactions on Evolutionary Computation
A Simulated Annealing-Based Multiobjective Optimization Algorithm: AMOSA
IEEE Transactions on Evolutionary Computation
Some new indexes of cluster validity
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A weighted sum validity function for clustering with a hybrid niching genetic algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fuzzy based Impulse Noise Reduction Method
Multimedia Tools and Applications
Data weighing mechanisms for clustering ensembles
Computers and Electrical Engineering
Effects of resampling method and adaptation on clustering ensemble efficacy
Artificial Intelligence Review
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Most clustering algorithms operate by optimizing (either implicitly or explicitly) a single measure of cluster solution quality. Such methods may perform well on some data sets but lack robustness with respect to variations in cluster shape, proximity, evenness and so forth. In this paper, we have proposed a multiobjective clustering technique which optimizes simultaneously two objectives, one reflecting the total cluster symmetry and the other reflecting the stability of the obtained partitions over different bootstrap samples of the data set. The proposed algorithm uses a recently developed simulated annealing-based multiobjective optimization technique, named AMOSA, as the underlying optimization strategy. Here, points are assigned to different clusters based on a newly defined point symmetry-based distance rather than the Euclidean distance. Results on several artificial and real-life data sets in comparison with another multiobjective clustering technique, MOCK, three single objective genetic algorithm-based automatic clustering techniques, VGAPS clustering, GCUK clustering and HNGA clustering, and several hybrid methods of determining the appropriate number of clusters from data sets show that the proposed technique is well suited to detect automatically the appropriate number of clusters as well as the appropriate partitioning from data sets having point symmetric clusters. The performance of AMOSA as the underlying optimization technique in the proposed clustering algorithm is also compared with PESA-II, another evolutionary multiobjective optimization technique.