Evolução da semissupervisão em detecção online de agrupamentos

The huge amount of currently available data puts considerable constraints on the task of information retrieval. Automatic methods to organize data, such as clustering, can be used to help with this task allowing timely access. Semi-supervised clustering approaches employ some additional information to guide the clustering performed based on data attributes to a more suitable data partition. However, this extra information may change over time imposing a shift in the manner by which data is organized. In order to help cope with this issue, this dissertation proposes the framework called CABESS (Cluster Adaptation Based on Evolving Semi-Supervision), for online clustering. This framework is able to deal with evolving semi-supervision obtained through user binary feedbacks. To validate the approach, the experiments were run over seven hierarchical labeled datasets considering clustering splits and merges over time. The experimental results show the potential of the proposed framework for dealing with evolving semi-supervision. Moreover, they also show that the framework is faster than traditional semi-supervised clustering algorithms using lower standard semi-supervision.CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível SuperiorCNPq - Conselho Nacional de Desenvolvimento Científico e TecnológicoFAPEMIG - Fundação de Amparo a Pesquisa do Estado de Minas GeraisUFU - Universidade Federal de UberlândiaDissertação (Mestrado)A disponibilidade abundante de dados torna inviável a busca manual por informações relevantes. Os métodos automáticos para organizar os dados, como a detecção de agrupamentos, podem ser úteis para ajudar nesta tarefa propiciando o acesso à informação desejada em tempo hábil. As abordagens de detecção semissupervisionada de agrupamentos empregam alguma informação adicional para guiar o processo baseado nos atributos dos dados de forma a obter uma organização mais próxima da desejada pelo usuário. Todavia, a informação extra pode mudar ao longo do tempo impondo uma mudança na maneira como os dados devem ser organizados. Para ajudar a lidar com esse problema, propõe-se o framework CABESS (Cluster Adaptation Based on Evolving Semi-Supervision), para detecção online de agrupamentos semissupervisionada. O framework é capaz de lidar com a evolução da semissupervisão obtida a partir de feedbacks binários do usuário. Para validar a abordagem, os experimentos foram executados sobre sete conjuntos de dados com rótulos baseados em hierarquia considerando a especialização e generalização dos agrupamentos ao longo do tempo. Os resultados experimentais mostram o potencial do framework proposto para lidar com a evolução da semissupervisão. Além disso, eles também mostram que o framework é mais rápido que os tradicionais algoritmos de detecção de agrupamentos semissupervisionados, mesmo usando um tipo pobre de especificação da semissupervisão

An analytical inspection framework for evaluating the search tactics and user profiles supported by information seeking interfaces

Searching is something we do everyday both in digital and physical environments. Whether we are searching for books in a library or information on the web, search is becoming increasingly important. For many years, however, the standard for search in software has been to provide a keyword search box that has, over time, been embellished with query suggestions, Boolean operators, and interactive feedback. More recent research has focused on designing search interfaces that better support exploration and learning. Consequently, the aim of this research has been to develop a framework that can reveal to designers how well their search interfaces support different styles of searching behaviour.The primary contribution of this research has been to develop a usability evaluation method, in the form of a lightweight analytical inspection framework, that can assess both search designs and fully implemented systems. The framework, called Sii, provides three types of analyses: 1) an analysis of the amount of support the different features of a design provide; 2) an analysis of the amount of support provided for 32 known search tactics; and 3) an analysis of the amount of support provided for 16 different searcher profiles, such as those who are finding, browsing, exploring, and learning. The design of the framework was validated by six independent judges, and the results were positively correlated against the results of empirical user studies. Further, early investigations showed that Sii has a learning curve that begins at around one and a half hours, and, when using identical analysis results, different evaluators produce similar design revisions.For Search experts, building interfaces for their systems, Sii provides a Human-Computer Interaction evaluation method that addresses searcher needs rather than system optimisation. For Human-Computer Interaction experts, designing novel interfaces that provide search functions, Sii provides the opportunity to assess designs using the knowledge and theories generated by the Information Seeking community. While the research reported here is under controlled environments, future work is planned that will investigate the use of Sii by independent practitioners on their own projects