Vertical mining with incomplete data

Authors:
Faris Alqadah;Zhen Hu;Lawrence J. Mazlack
Affiliations:
Applied Computational Intelligence Laboratory, University of Cincinnati;Applied Computational Intelligence Laboratory, University of Cincinnati;Applied Computational Intelligence Laboratory, University of Cincinnati
Venue:
MAMECTIS'08 Proceedings of the 10th WSEAS international conference on Mathematical methods, computational techniques and intelligent systems
Year:
2008

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Abstract

Mining frequent patterns is essential in many data mining methods. Frequent patterns lead to the discovery of association rules, strong rules, sequential episodes, and multi-dimensional patterns. Patterns should be discovered in a time and space efficient manner. Vertical mining algorithms key advantage is that they can outperform their horizontal counterparts in terms of both time and space efficiency. Little work has addressed how incomplete data influences vertical data mining. Therefore, the quality and utility of vertical mining algorithms results remains uncertain as real data sets often contain incomplete data. This paper considers establishing methodologies that deal with incomplete data in vertical mining.