Visualização e análise de agrupamentos usando redes auto-organizáveis, segmentação de imagens e índices de validação

Estratégias de Detecção de Anomalias de Modularidade em Sistemas Orientados a Aspectos

José Alfredo F. CostaMárcio L. GonçalvesMárcio L. de Andrade NettoBianca A. C. S. Costa

Isela Macía BertránAlessandro GarciaArndt von Staa

Self-organizing maps (SOM) had been widely used for input data quantization and visual display of data, an important property that does not exist in most of clustering algorithms. Effective data clustering using SOM involves two or three steps procedure. After proper network training, units can be clustered generating regions of neurons which are related to data clusters. The basic assumption relies on the data density approximation by the neurons through unsupervised learning. The transformation of high dimensional data into images and its visualization and clustering is addressed in this paper. The proposed method segments SOM networks via watershed algorithms and modified cluster validation indexes. Results are shown for benchmark datasets for different map sizes. Several modularity flaws in aspect oriented programming (AOP) are significantly different from those found in object-oriented (OO) systems. Unfortunately, the set of properly documented flaws in AOP is limited, and they mostly refer to classical well-known flaws in OO programming. This work defines a supplementary catalog of AOP flaws related to: (i) inappropriate definition of pointcuts, and (ii) problems associated with undesirable aspect interdependencies. Furthermore, detection strategies and refactoring rules are defined to respectively assist flaws' identification and removal.

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