3 research outputs found

    Enhancing cluster analysis with explainable AI and multidimensional cluster prototypes

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    Explainable Artificial Intelligence (XAI) aims to introduce transparency and intelligibility into the decision-making process of AI systems. Most often, its application concentrates on supervised machine learning problems such as classification and regression. Nevertheless, in the case of unsupervised algorithms like clustering, XAI can also bring satisfactory results. In most cases, such application is based on the transformation of an unsupervised clustering task into a supervised one and providing generalised global explanations or local explanations based on cluster centroids. However, in many cases, the global explanations are too coarse, while the centroid-based local explanations lose information about cluster shape and distribution. In this paper, we present a novel approach called ClAMP (Cluster Analysis with Multidimensional Prototypes) that aids experts in cluster analysis with human-readable rule-based explanations. The developed state-of-the-art explanation mechanism is based on cluster prototypes represented by multidimensional bounding boxes. This allows representing of arbitrary shaped clusters and combines the strengths of local explanations with the generality of global ones. We demonstrate and evaluate the use of our approach in a real-life industrial case study from the domain of steel manufacturing as well as on the benchmark datasets. The explanations generated with ClAMP were more precise than either centroid-based or global ones

    Checking and improving business process models in BPMN2

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    Business Process Modeling (BPM) is a systems engineering activity where we rep- resent the processes of an enterprise, so they can be shared, understood and improved. Despite the set of innovative tools for BPM modelling that exist in the market, they allow modelers to introduce errors during the modelling process. As there is no idea which errors the tools do not detect, what are the most recurrent errors and how could this prob- lem be mitigated, this dissertation presents a study and a proposal to help solving this problem. Firstly, a tool survey was developed to describe the state of the practice on the ability of Modelling Tools to validate BPMN2 models and determine the most recurrent defects introduced by BPMN modellers. Secondly, based on an empirical study using the QUASAR validator we provide evidence on its ability to validate a set of well-formedness rules and best practices and therefore detect errors in BPMN2 Models. Finally, we want to understand if this metamodelling-based validation facility can be used to prevent intro- ducing modelling errors, while speeding up the learning curve.A Modelação de Processos de Negócio (MPN) é uma atividade de engenharia de sistemas onde representamos os processos de uma empresa, para que os mesmos possam ser partilhados, compreendidos e melhorados. Apesar do elevado número de ferramentas de MPN existentes no mercado, estas permitem aos modeladores introduzir erros du- rante o processo de modelação. Como não existe uma ideia clara acerca de quais os erros que as ferramentas não detetam, quais os erros cometidos mais recorrentemente e como o problema pode ser resolvido, esta dissertação apresenta um estudo e uma proposta para resolver o problema. Inicialmente foi efetuado um levantamento do estado da prática da capacidade das ferramentas de modelação para validar os modelos em BPMN2, e determinar os erros mais frequentemente introduzidos pelos modeladores. Em seguida, baseado num estudo empírico, usando o validador QUASAR, fornecemos evidências sobre a sua capacidade para validar o conjunto de regras de boa formação e boas práticas na modelação de processos de negócio e assim detetar os erros introduzidos nos modelos em BPMN2. Finalmente, queremos compreender se esta facilidade de validação baseada em metamodelos pode ser usada para prevenir a introdução de erros durante o processo de modelação de processos de negócio, acelerando assim a curva de aprendizagem do modelador

    at the 14th Conference of the Spanish Association for Artificial Intelligence (CAEPIA 2011)

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    Technical Report TR-2011/1, Department of Languages and Computation. University of Almeria November 2011. Joaquín Cañadas, Grzegorz J. Nalepa, Joachim Baumeister (Editors)The seventh workshop on Knowledge Engineering and Software Engineering (KESE7) was held at the Conference of the Spanish Association for Artificial Intelligence (CAEPIA-2011) in La Laguna (Tenerife), Spain, and brought together researchers and practitioners from both fields of software engineering and artificial intelligence. The intention was to give ample space for exchanging latest research results as well as knowledge about practical experience.University of Almería, Almería, Spain. AGH University of Science and Technology, Kraków, Poland. University of Würzburg, Würzburg, Germany
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