Visualización de conjunto de datos de múltiples instancias

Abstract

This degree work addresses the problem of the visualization of data sets of multiple instances (MI), seeking to understand the particularities of these data sets and their relationships. As there are few works related to this topic in the literature, it is considered that the result may be useful for those who currently work with the multi-instance learning paradigm (MIL). Thus, the intention of this work is to develop a visualization method that allows users to understand what the relationships or hidden patterns in MI data sets. To this end, an important research question is posed, what visualization methods can be adapted to explore MI data sets? The answer to the research question is sought by creating a visualization proposal and experimenting with different visualization methods on the data sets. The visualization proposal was validated through surveys and questionnaires to MIL experts in addition to internal tests and comparisons. The experiments carried out showed that using combined visualization methods allows extracting more information from the data set. Taking this into account and following the recommendations of the experts, it would be good to create tools that allow representing a set of MI in different visualization methods and in turn make more intuitive tools, so that the data visualization process is faster and more effective in pattern detection.