Algorithms for clustering data
Algorithms for clustering data
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Optimal combinations of pattern classifiers
Pattern Recognition Letters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scale-based approach to hierarchical fuzzy clustering
Signal Processing - Special issue on fuzzy logic in signal processing
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Estimation of generalized mixtures and its application in image segmentation
IEEE Transactions on Image Processing
Image segmentation and labeling using the Polya urn model
IEEE Transactions on Image Processing
Immune-based evolutionary algorithm for fabric evaluation
Mathematics and Computers in Simulation
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This paper proposes the use of more than one clustering method to improve clustering performance. Clustering is an optimization procedure based on a specific clustering criterion. Clustering combination can be regarded as a technique that constructs and processes multiple clustering criteria. Since the global and local clustering criteria are complementary rather than competitive, combining these two types of clustering criteria may enhance the clustering performance. In our past work, a multi-objective programming based simultaneous clustering combination algorithm has been proposed, which incorporates multiple criteria into an objective function by a weighting method, and solves this problem with constrained nonlinear optimization programming. But this algorithm has high computational complexity. Here a sequential combination approach is investigated, which first uses the global criterion based clustering to produce an initial result, then uses the local criterion based information to improve the initial result with a probabilistic relaxation algorithm or linear additive model. Compared with the simultaneous combination method, sequential combination has low computational complexity. Results on some simulated data and standard test data are reported. It appears that clustering performance improvement can be achieved at low cost through sequential combination.