Comparative Analysis of Decision Tree Algorithms for Data Warehouse Fragmentation

Palabras clave: Data analysis, computer systems, databases, artificial intelligence, decision making


One of the main problems faced by Data Warehouse designers is fragmentation.
Several studies have proposed data mining-based horizontal fragmentation methods.
However, not exists a horizontal fragmentation technique that uses a decision tree. This paper presents the analysis of different decision tree algorithms to select the best one to implement the fragmentation method. Such analysis was performed under version 3.9.4 of Weka, considering four evaluation metrics (Precision, ROC Area, Recall and F-measure) for different selected data sets using the Star Schema Benchmark. The results showed that the two best algorithms were J48 and Random Forest in most cases; nevertheless, J48 was selected because it is more efficient in building the model.


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Biografía del autor/a

Nidia Rodríguez Mazahua, Tecnológico Nacional de México

Mtra. in Administrative Engineering

Lisbeth Rodríguez Mazahua, Tecnológico Nacional de México

PhD in Computer Science

Asdrúbal López Chau, Centro Universitario UAEM

PhD in Computer Science

Giner Alor Hernández, Tecnológico Nacional de México

PhD of Science in the specialty of Electrical Engineering

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Cómo citar
Rodríguez Mazahua, N., Rodríguez Mazahua, L., López Chau, A., & Hernández, G. A. (2020). Comparative Analysis of Decision Tree Algorithms for Data Warehouse Fragmentation. Revista Perspectiva Empresarial, 7(2 Supl.1), 31-43.
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