Luciano D. S. Pacifico

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Clustering methods aims to organize a set of items into clusters such that items within a given cluster have a high degree of similarity, while items belonging to different clusters have a high degree of dissimilarity. The self-organizing map (SOM) introduced by Kohonen is an unsupervised competitive learning neural network method which has both clustering(More)
Extreme Learning Machine (ELM) is a new learning method for single-hidden layer feedforward neural network (SLFN) training. ELM approach increases the learning speed by means of randomly generating input weights and biases for hidden nodes rather than tuning network parameters, making this approach much faster than traditional gradient-based ones. However,(More)
Extreme Learning Machine (ELM) is a learning method for single-hidden layer feedforward neural network (SLFN) training. ELM approach increases the learning speed by means of randomly generating input weights and biases for hidden nodes rather than tuning network parameters, making this approach much faster than traditional gradient-based one. In this paper,(More)
Group Search Optimization (GSO) is a Swarm Intelligence (SI) approach for continuous optimization problems inspired by animal searching behavior and group living theory. The Artificial Fish Swarm (AFS) is an intelligent optimization algorithm based on the behavior of fish. In this paper, a new hybrid group search optimization method is presented, using the(More)
Data clustering is an important tool for statistical data analysis and exploration, and it has been successfully applied in many fields like image understanding, bioinformatics, big data mining, and so on. From the past few decades, Evolutionary Algorithms (EAs) have been introduced to deal with clustering task, given their global search capabilities and(More)
Clustering analysis aims to distribute a dataset in-groups in such a way that individuals from the same group have a high degree of similarity among each other, while individuals from different groups have a high degree of dissimilarity among each other. Clustering analysis has become an important mechanism for data exploration and understanding.(More)