Title :
The Cassiopeia Model: Using summarization and clusterization for semantic knowledge management
Author :
Guelpeli, Marcus V C ; Garcia, Cristina Bicharra ; Branco, António Horta
Author_Institution :
Dept. de Cienc. da Comput., Univ. Fed. Fluminense - UFF, Rio de Janeiro, Brazil
Abstract :
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.
Keywords :
data mining; knowledge management; pattern clustering; text analysis; Cassiopeia model; attribute selection; cluster measurement; knowledge discovery; scientific literature; semantic knowledge management; summarization; text clusterization; Clustering algorithms; Manuals; Knowledge Discovery; Summarization and Clusterization; Text mining;
Conference_Titel :
Applications of Digital Information and Web Technologies (ICADIWT), 2011 Fourth International Conference on the
Conference_Location :
Stevens Point, WI
Print_ISBN :
978-1-4244-9824-6
DOI :
10.1109/ICADIWT.2011.6041409
