Le pronostic industriel vise à étendre le cycle de vie d’un dispositif physique, tout en réduisant les couts d’exploitation et de maintenance. Pour cette raison, le pronostic est considéré comme un processus clé avec des capacités de prédiction. En effet, des estimations précises de la durée de vie avant défaillance d’un équipement, Remaining Useful Life (RUL), permettent de mieux définir un plan d’action visant à accroitre la sécurité, réduire les temps d’arrêt, assurer l’achèvement de la mission et l’efficacité de la production.Des études récentes montrent que les approches guidées par les données sont de plus en plus appliquées pour le pronostic de défaillance. Elles peuvent être considérées comme des modèles de type boite noire pour l’ étude du comportement du système directement `a partir des données de surveillance d’ état, pour définir l’ état actuel du système et prédire la progression future de défauts. Cependant, l’approximation du comportement des machines critiques est une tâche difficile qui peut entraîner des mauvais pronostic. Pour la compréhension de la modélisation du pronostic guidé par les données, on considère les points suivants. 1) Comment traiter les données brutes de surveillance pour obtenir des caractéristiques appropriées reflétant l’ évolution de la dégradation? 2) Comment distinguer les états de dégradation et définir des critères de défaillance (qui peuvent varier d’un cas `a un autre)? 3) Comment être sûr que les modèles définis seront assez robustes pour montrer une performance stable avec des entrées incertaines s’ écartant des expériences acquises, et seront suffisamment fiables pour intégrer des données inconnues (c’est `a dire les conditions de fonctionnement, les variations de l’ingénierie, etc.)? 4) Comment réaliser facilement une intégration sous des contraintes et des exigence industrielles? Ces questions sont des problèmes abordés dans cette thèse. Elles ont conduit à développer une nouvelle approche allant au-delà des limites des méthodes classiques de pronostic guidé par les données. / Prognostics and Health Management (PHM) aims at extending the life cycle of engineerin gassets, while reducing exploitation and maintenance costs. For this reason,prognostics is considered as a key process with future capabilities. Indeed, accurateestimates of the Remaining Useful Life (RUL) of an equipment enable defining furtherplan of actions to increase safety, minimize downtime, ensure mission completion andefficient production.Recent advances show that data-driven approaches (mainly based on machine learningmethods) are increasingly applied for fault prognostics. They can be seen as black-boxmodels that learn system behavior directly from Condition Monitoring (CM) data, usethat knowledge to infer its current state and predict future progression of failure. However,approximating the behavior of critical machinery is a challenging task that canresult in poor prognostics. As for understanding, some issues of data-driven prognosticsmodeling are highlighted as follows. 1) How to effectively process raw monitoringdata to obtain suitable features that clearly reflect evolution of degradation? 2) Howto discriminate degradation states and define failure criteria (that can vary from caseto case)? 3) How to be sure that learned-models will be robust enough to show steadyperformance over uncertain inputs that deviate from learned experiences, and to bereliable enough to encounter unknown data (i.e., operating conditions, engineering variations,etc.)? 4) How to achieve ease of application under industrial constraints andrequirements? Such issues constitute the problems addressed in this thesis and have ledto develop a novel approach beyond conventional methods of data-driven prognostics.

Diversos métodos têm sido desenvolvidos para a organização da crescente quantidade de documentos textuais. Esses métodos frequentemente fazem uso de algoritmos de agrupamento para organizar documentos que referem-se a um mesmo assunto em um mesmo grupo, supondo que conteúdos de documentos de um mesmo grupo são similares. Porém, existe a possibilidade de que documentos pertencentes a grupos distintos também apresentem características semelhantes. Considerando esta situação, há a necessidade de desenvolver métodos que possibilitem a organização flexível de documentos, ou seja, métodos que possibilitem que documentos sejam organizados em diferentes grupos com diferentes graus de compatibilidade. O agrupamento fuzzy de documentos textuais apresenta-se como uma técnica adequada para este tipo de organização, uma vez que algoritmos de agrupamento fuzzy consideram que um mesmo documento pode ser compatível com mais de um grupo. Embora tem-se desenvolvido algoritmos de agrupamento fuzzy que possibilitam a organização flexível de documentos, tal organização é avaliada em termos do desempenho do agrupamento de documentos. No entanto, considerando que grupos de documentos devem possuir descritores que identifiquem adequadamente os tópicos representados pelos mesmos, de maneira geral os descritores de grupos tem sido extraídos utilizando alguma heurística sobre um conjunto pequeno de documentos, realizando assim, uma avaliação simples sobre o significado dos grupos extraídos. No entanto, uma apropriada extração e avaliação de descritores de grupos é importante porque os mesmos são termos representantes da coleção que identificam os tópicos abordados nos documentos. Portanto, em aplicações em que o agrupamento fuzzy é utilizado para a organização flexível de documentos, uma descrição apropriada dos grupos obtidos é tão importante quanto um bom agrupamento, uma vez que, neste tipo de agrupamento, um mesmo descritor pode indicar o conteúdo de mais de um grupo. Essa necessidade motivou esta tese, cujo objetivo foi investigar e desenvolver métodos para a extração de descritores de grupos fuzzy para a organização flexível de documentos. Para cumprir esse objetivo desenvolveu se: i) o método SoftO-FDCL (Soft Organization - Fuzzy Description Comes Last ), pelo qual descritores de grupos fuzzy at são extraídos após o processo de agrupamento fuzzy, visando identicar tópicos da organização flexível de documentos independentemente do algoritmo de agrupamento fuzzy utilizado; ii) o método SoftO-wFDCL ( Soft Organization - weighted Fuzzy Description Comes Last ), pelo qual descritores de grupos fuzzy at também são extraídos após o processo de agrupamento fuzzy utilizando o grau de pertinência dos documentos em cada grupo, obtidos do agrupamento fuzzy, como fator de ponderação dos termos candidatos a descritores; iii) o método HSoftO-FDCL (Hierarchical Soft Organization - Fuzzy Description Comes Last ), pelo qual descritores de grupos fuzzy hierárquicos são extraídos após o processo de agrupamento hierárquico fuzzy, identificando tópicos da organização hierárquica flexível de documentos. Adicionalmente, apresenta-se nesta tese uma aplicação do método SoftO-FDCL no contexto do programa de educação médica continuada canadense, reforçando a utilidade e aplicabilidade da organização flexível de documentos / Several methods have been developed to organize the growing number of textual documents. Such methods frequently use clustering algorithms to organize documents with similar topics into clusters. However, there are situations when documents of dffierent clusters can also have similar characteristics. In order to overcome this drawback, it is necessary to develop methods that permit a soft document organization, i.e., clustering documents into different clusters according to different compatibility degrees. Among the techniques that we can use to develop methods in this sense, we highlight fuzzy clustering algorithms (FCA). By using FCA, one of the most important steps is the evaluation of the yield organization, which is performed considering that all analyzed topics are adequately identified by cluster descriptors. In general, cluster descriptors are extracted using some heuristic over a small number of documents. The adequate extraction and evaluation of cluster descriptors is important because they are terms that represent the collection and identify the topics of the documents. Therefore, an adequate description of the obtained clusters is as important as a good clustering, since the same descriptor might identify one or more clusters. Hence, the development of methods to extract descriptors from fuzzy clusters obtained for soft organization of documents motivated this thesis. Aiming at investigating such methods, we developed: i) the SoftO-FDCL (Soft Organization - Fuzzy Description Comes Last) method, in which descriptors of fuzzy clusters are extracted after clustering documents, identifying topics regardless the adopted fuzzy clustering algorithm; ii) the SoftO-wFDCL (Soft Organization - weighted Fuzzy Description Comes Last) method, in which descriptors of fuzzy clusters are also extracted after the fuzzy clustering process using the membership degrees of the documents as a weighted factor for the candidate descriptors; iii) the HSoftO-FDCL (Hierarchical Soft Organization - Fuzzy Description Comes Last) method, in which descriptors of hierarchical fuzzy clusters are extracted after the hierarchical fuzzy clustering process, identifying topics by means of a soft hierarchical organization of documents. Besides presenting these new methods, this thesis also discusses the application of the SoftO-FDCL method on documents produced by the Canadian continuing medical education program, presenting the utility and applicability of the soft organization of documents in real-world scenario

We propose a clustering algorithm which incorporates a similarity-based fuzzy clustering and principal component analysis. The proposed algorithm is capable of discovering clusters with hyper-spherical, hyperellipsoidal, or oblique hyper-ellipsoidal shapes. Besides, the number of the clusters need not be specified in advance by the user. For a given dataset, the orientation, locations, and the number of clusters obtained can truthfully reflect the characteristics of the dataset. Experimental results, obtained by running on datasets generated synthetically, show that our method performs better than other methods.

Recommender systems are information retrieval tools helping users in their information seeking tasks and guiding them in a large space of possible options. Many hybrid recommender systems are proposed so far to overcome shortcomings born of pure content-based (PCB) and pure collaborative filtering (PCF) systems. Most studies on recommender systems aim to improve the accuracy and efficiency of predictions. In this thesis, we propose an online hybrid recommender strategy (CBCFdfc) based on content boosted collaborative filtering algorithm which aims to improve the prediction accuracy and efficiency. CBCFdfc combines content-based and collaborative characteristics to solve problems like sparsity, new item and over-specialization. CBCFdfc uses fuzzy clustering to keep a certain level of prediction accuracy while decreasing online prediction time. We compare CBCFdfc with PCB and PCF according to prediction accuracy metrics, and with CBCFonl (online CBCF without clustering) according to online recommendation time. Test results showed that CBCFdfc performs better than other approaches in most cases. We, also, evaluate the effect of user-specified parameters to the prediction accuracy and efficiency. According to test results, we determine optimal values for these parameters. In addition to experiments made on simulated data, we also perform a user study and evaluate opinions of users about recommended movies. The results that are obtained in user evaluation are satisfactory. As a result, the proposed system can be regarded as an accurate and efficient hybrid online movie recommender.

Feature clustering is a powerful method to reduce the dimensionality of feature vectors for text classification. In this paper, we propose a fuzzy similarity-based self-constructing algorithm for feature clustering. The words in the feature vector of a document set are grouped into clusters based on similarity test. Words that are similar to each other are grouped into the same cluster. Each cluster is characterized by a membership function with statistical mean and deviation. When all the words have been fed in, a desired number of clusters are formed automatically. We then have one extracted feature for each cluster. The extracted feature corresponding to a cluster is a weighted combination of the words contained in the cluster. By this algorithm, the derived membership functions match closely with and describe properly the real distribution of the training data. Besides, the user need not specify the number of extracted features in advance, and trial-and-error for determining the appropriate number of extracted features can then be avoided. 20 Newsgroups data set and Cade 12 web directory are introduced to be our experimental data. We adopt the support vector machine to classify the documents. Experimental results show that our method can run faster and obtain better extracted features than other methods.

Dans cette thèse, nous proposons une méthode pour la visualisation d'ensembles chevauchant et de basé sur les diagrammes d'Euler. Les diagrammes d'Euler sont probablement les plus intuitifs pour représenter de manière schématique les ensembles qui partagent des éléments. Cette métaphore visuelle est ainsi un outil puissant en termes de visualisation d'information. Cependant, la génération automatique de ces diagrammes présente encore de nombreux problèmes difficiles. Premièrement, tous les clustering chevauchants ne peuvent pas être dessinées avec les diagrammes d'Euler classiques. Deuxièmement, la plupart des algorithmes existants permettent uniquement de représenter les diagrammes de dimensions modestes. Troisièmement, les besoins des applications réelles requièrent un processus plus fiable et plus rapide.Dans cette thèse, nous décrivons une version étendue des diagrammes d'Euler. Cette extension permet de modéliser l'ensemble des instances de la classe des clustering chevauchants. Nous proposons ensuite un algorithme automatique de génération de cette extension des diagrammes d'Euler. Enfin, nous présentons une implémentation logicielle et des expérimentations de ce nouvel algorithme. / In this thesis, we propose a method for the visualisation of overlapping sets and of fuzzy graph clusterings based on Euler diagrams.Euler diagrams are probably the most intuitive and most used method to depict sets in which elements can be shared. Such a powerful visualisation metaphor could be an invaluable visualisation tool, but the automatic generation of Euler diagrams still presents many challenging problems. First, not all instances can be drawn using standard Euler diagrams. Second, most existing algorithms focus on diagrams of modest dimensions while real-world applications typically features much larger data. Third, the generation process must be reliable and reasonably fast.In this thesis, we describe an extended version of Euler diagrams that can be produced for every input instance. We then propose an automatic procedure for the generation of such diagrams that specifically target large input instances. Finally, we present a software implementation of this method and we describe some output examples generated on real-world data.

Diversos métodos têm sido desenvolvidos para a organização da crescente quantidade de documentos textuais. Esses métodos frequentemente fazem uso de algoritmos de agrupamento para organizar documentos que referem-se a um mesmo assunto em um mesmo grupo, supondo que conteúdos de documentos de um mesmo grupo são similares. Porém, existe a possibilidade de que documentos pertencentes a grupos distintos também apresentem características semelhantes. Considerando esta situação, há a necessidade de desenvolver métodos que possibilitem a organização flexível de documentos, ou seja, métodos que possibilitem que documentos sejam organizados em diferentes grupos com diferentes graus de compatibilidade. O agrupamento fuzzy de documentos textuais apresenta-se como uma técnica adequada para este tipo de organização, uma vez que algoritmos de agrupamento fuzzy consideram que um mesmo documento pode ser compatível com mais de um grupo. Embora tem-se desenvolvido algoritmos de agrupamento fuzzy que possibilitam a organização flexível de documentos, tal organização é avaliada em termos do desempenho do agrupamento de documentos. No entanto, considerando que grupos de documentos devem possuir descritores que identifiquem adequadamente os tópicos representados pelos mesmos, de maneira geral os descritores de grupos tem sido extraídos utilizando alguma heurística sobre um conjunto pequeno de documentos, realizando assim, uma avaliação simples sobre o significado dos grupos extraídos. No entanto, uma apropriada extração e avaliação de descritores de grupos é importante porque os mesmos são termos representantes da coleção que identificam os tópicos abordados nos documentos. Portanto, em aplicações em que o agrupamento fuzzy é utilizado para a organização flexível de documentos, uma descrição apropriada dos grupos obtidos é tão importante quanto um bom agrupamento, uma vez que, neste tipo de agrupamento, um mesmo descritor pode indicar o conteúdo de mais de um grupo. Essa necessidade motivou esta tese, cujo objetivo foi investigar e desenvolver métodos para a extração de descritores de grupos fuzzy para a organização flexível de documentos. Para cumprir esse objetivo desenvolveu se: i) o método SoftO-FDCL (Soft Organization - Fuzzy Description Comes Last ), pelo qual descritores de grupos fuzzy at são extraídos após o processo de agrupamento fuzzy, visando identicar tópicos da organização flexível de documentos independentemente do algoritmo de agrupamento fuzzy utilizado; ii) o método SoftO-wFDCL ( Soft Organization - weighted Fuzzy Description Comes Last ), pelo qual descritores de grupos fuzzy at também são extraídos após o processo de agrupamento fuzzy utilizando o grau de pertinência dos documentos em cada grupo, obtidos do agrupamento fuzzy, como fator de ponderação dos termos candidatos a descritores; iii) o método HSoftO-FDCL (Hierarchical Soft Organization - Fuzzy Description Comes Last ), pelo qual descritores de grupos fuzzy hierárquicos são extraídos após o processo de agrupamento hierárquico fuzzy, identificando tópicos da organização hierárquica flexível de documentos. Adicionalmente, apresenta-se nesta tese uma aplicação do método SoftO-FDCL no contexto do programa de educação médica continuada canadense, reforçando a utilidade e aplicabilidade da organização flexível de documentos / Several methods have been developed to organize the growing number of textual documents. Such methods frequently use clustering algorithms to organize documents with similar topics into clusters. However, there are situations when documents of dffierent clusters can also have similar characteristics. In order to overcome this drawback, it is necessary to develop methods that permit a soft document organization, i.e., clustering documents into different clusters according to different compatibility degrees. Among the techniques that we can use to develop methods in this sense, we highlight fuzzy clustering algorithms (FCA). By using FCA, one of the most important steps is the evaluation of the yield organization, which is performed considering that all analyzed topics are adequately identified by cluster descriptors. In general, cluster descriptors are extracted using some heuristic over a small number of documents. The adequate extraction and evaluation of cluster descriptors is important because they are terms that represent the collection and identify the topics of the documents. Therefore, an adequate description of the obtained clusters is as important as a good clustering, since the same descriptor might identify one or more clusters. Hence, the development of methods to extract descriptors from fuzzy clusters obtained for soft organization of documents motivated this thesis. Aiming at investigating such methods, we developed: i) the SoftO-FDCL (Soft Organization - Fuzzy Description Comes Last) method, in which descriptors of fuzzy clusters are extracted after clustering documents, identifying topics regardless the adopted fuzzy clustering algorithm; ii) the SoftO-wFDCL (Soft Organization - weighted Fuzzy Description Comes Last) method, in which descriptors of fuzzy clusters are also extracted after the fuzzy clustering process using the membership degrees of the documents as a weighted factor for the candidate descriptors; iii) the HSoftO-FDCL (Hierarchical Soft Organization - Fuzzy Description Comes Last) method, in which descriptors of hierarchical fuzzy clusters are extracted after the hierarchical fuzzy clustering process, identifying topics by means of a soft hierarchical organization of documents. Besides presenting these new methods, this thesis also discusses the application of the SoftO-FDCL method on documents produced by the Canadian continuing medical education program, presenting the utility and applicability of the soft organization of documents in real-world scenario

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