The Cassiopeia Model: Using summarization and clusterization for semantic knowledge management

This work proposes a comparative study of algorithms used for attribute selection in text clusterization in the scientific literature with the Cassiopeia algorithm. The aim of the Cassiopeia model is to allow for knowledge Discovery in textual bases in distinct and/or antagonistic domains using both Summarization and Clusterizations as part of the process of obtaining this knowledge. Hence, our intention is to achieve an improvement in the measurement of clusters as well as to solve the problem of high dimensionality in the knowledge discovery of textual bases.