A data mining approach to computational taxonomy

• Autores: Perichinsky, Gregorio; García Martínez, Ramón
• Año de publicación: 2000
• Idioma: inglés
• Tipo de recurso: documento de conferencia
• Materia: Ciencias Informáticas; Computational Taxonomy; Taxonomy; Insufficient database; Knowledge discovery; Data mining; base de datos; Clustering
• Institución: Universidad Nacional de La Plata

This study investigates an approach of knowledge discovery and data mining in insufficient databases. An application of Computational Taxonomy analysis demonstrates that the approach is effective in such a data mining process. The approach is characterized by the use ot both the second type of domain knowledge and visualization. This type of knowledge is newly defined in this study and deduced from supposition about background situations of the domain. The supposition is triggered by strong intuition about the extracted features in a recurrent process of data mining. This type of domain knowledge is useful not only for discovering interesting knowledge but al so tor guiding the subsequent search for more explicit and interesting knowledge. The visualization is very useful for triggering the supposition.
Eje: Ingeniería de software y base de datos
Red de Universidades con Carreras en Informática (RedUNCI)