A Novel Incremental Algorithm for Frequent Itemsets Mining in Dynamic Datasets

Authors:
Raudel Hernández-León;José Hernández-Palancar;J. A. Carrasco-Ochoa;J. Fco. Martínez-Trinidad
Affiliations:
Advanced Technologies Application Center (CENATAV), La Habana, Cuba C.P. 12200 and Computer Science Department National Institute of Astrophysics, Optics and Electronics, Puebla, Mexico CP:72840;Advanced Technologies Application Center (CENATAV), La Habana, Cuba C.P. 12200;Computer Science Department National Institute of Astrophysics, Optics and Electronics, Puebla, Mexico CP:72840;Computer Science Department National Institute of Astrophysics, Optics and Electronics, Puebla, Mexico CP:72840
Venue:
CIARP '08 Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications
Year:
2008

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Abstract

Frequent Itemsets (FI) Mining is one of the most researched areas of data mining. When some new transactions are appended, deleted or modified in a dataset, updating FI is a nontrivial task since such updates may invalidate existing FI or introduce new ones. In this paper a novel algorithm suitable for FI mining in dynamic datasets named Incremental Compressed Arrays is presented. In the experiments, our algorithm was compared against some algorithms as Eclat, PatriciaMine and FP-growth when new transactions are added or deleted.