Subspace clustering is the task of identifying clusters in subspaces of the input dimensions of a given dataset. Noisy data in certain attributes cause difficulties for traditional clustering algorithms, because the high discrepancies within them can make objects appear too different to be grouped in the same cluster. This requires methods specially designed for subspace clustering. This paper presents our second approach to subspace and projected clustering based on self-organizing maps (SOMs), which is a local adaptive receptive field dimension selective SOM. By introducing a time-variant topology, our method is an improvement in terms of clustering quality, computational cost, and parameterization. This enables the method to identify the correct number of clusters and their respective relevant dimensions, and thus it presents nearly perfect results in synthetic datasets and surpasses our previous method in most of the real-world datasets considered.

There has been an increasing interest in semisupervised learning in the recent years because of the great number of datasets with a large number of unlabeled data but only a few labeled samples. Semi-supervised learning algorithms can work with both types of data, combining them to obtain better performance for both clustering and classification. Also, these datasets commonly have a high number of dimensions. This article presents a new semi-supervised method based on selforganizing maps (SOMs) for clustering and classification, called Semi-Supervised Self-Organizing Map (SS-SOM). The method can dynamically switch between supervised and unsupervised learning during the training according to the availability of the class labels for each pattern. Our results show that the SS-SOM outperforms other semi-supervised methods in conditions in which there is a low amount of labeled samples, also achieving good results when all samples are labeled.

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When working with decomposition-based algorithms, an appropriate set of weights might improve quality of the final solution. A set of uniformly distributed weights usually leads to well-distributed solutions on a Pareto front. However, there are two main difficulties with this approach. Firstly, it may fail depending on the problem geometry. Secondly, the population size becomes not flexible as the number of objectives increases. In this paper, we propose the MOEA/D with Uniformly Randomly Adaptive Weights (MOEA/D-URAW) which uses the Uniformly Randomly method as an approach to subproblems generation, allowing a flexible population size even when working with many objective problems. During the evolutionary process, MOEA/D-URAW adds and removes subproblems as a function of the sparsity level of the population. Moreover, instead of requiring assumptions about the Pareto front shape, our method adapts its weights to the shape of the problem during the evolutionary process. Experimental results using WFG41-48 problem classes, with different Pareto front shapes, shows that the present method presents better or equal results in 77.5% of the problems evaluated from 2 to 6 objectives when compared with state-of-the-art methods in the literature.

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Nowadays, with the advance of technology, there is an increasing amount of unstructured data being generated every day. However, it is a painful job to label and organize it. Labeling is an expensive, time-consuming, and difficult task. It is usually done manually, which collaborates with the incorporation of noise and errors to the data. Hence, it is of great importance to developing intelligent models that can benefit from both labeled and unlabeled data. Currently, works on unsupervised and semi-supervised learning are still being overshadowed by the successes of purely supervised learning. However, it is expected that they become far more important in the longer term. This article presents a semi-supervised model, called Batch Semi-Supervised Self-Organizing Map (Batch SS-SOM), which is an extension of a SOM incorporating some advances that came with the rise of Deep Learning, such as batch training. The results show that Batch SS-SOM is a good option for semisupervised classification and clustering. It performs well in terms of accuracy and clustering error, even with a small number of labeled samples, as well as when presented to unsupervised data, and shows competitive results in transfer learning scenarios in traditional image classification benchmark datasets.

Resumo-O monitoramento espaço-temporal da população do mosquito Aedes aegytpié de suma importância para as autoridades de saúde pública. A população do Aedesé estimada a partir da contagem de ovos capaturados em armadilhas chamadas ovitrampas. A tarefa de contagem, realizada atualmente com auxílio do microscópio,é desgastante para quem a executa e sujeita a diversos tipos de erros. O Sistema Autônomo de Reconhecimento e Contagem de Ovos-SARCO, foi desenvolvido para efetuar tais contagens automaticamente, com maior precisão e de forma mais rápida. Redes neurais artificiais combinadas com técnicas de processamento de imagem e de estatística, foram aplicadas em imagens digitalizadas de palhetas de ovitrampas para contar o número de ovos contidos nas mesmas. A segmentação das imagens utilizou apenas informações sobre cores, e o número de ovos foi estimado a partir daárea da imagem ocupada por ovos. Testes de validação utilizaram um conjunto de 18 imagens de ovitrampas com diferentes densidades de ovos, recolhidas junto aos programas de controle do vetor da dengue (SMTP-Aedes) nos municípios de Recife, Ipojuca e Santa Cruz do Capibaribe. Para este conjunto de dados, o SARCO obteve um erro de contagem da ordem de 12 %, bem inferior aos 18 % obtidos com microscópio. Todos os resultados foram validados estatisticamente através de testes de hipótese.