Trie: An alternative data structure for data mining algorithms

Authors:
F. Bodon;L. Rónyai
Affiliations:
Computer and Automation Institute, Hungarian Academy of Sciences Lágymányosi u. 11., H-1111 Budapest, Hungary;Computer and Automation Institute, Hungarian Academy of Sciences Lágymányosi u. 11., H-1111 Budapest, Hungary
Venue:
Mathematical and Computer Modelling: An International Journal
Year:
2003

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Abstract

Frequent itemset mining is one of the most important data mining fields. Most algorithms are APRIORI based, where hash-trees are used extensively to speed up the search for itemsets. In this paper, we demonstrate that a version of the trie data structure outperforms hash-trees in some data mining applications. Tries appear to offer simpler and scalable algorithms which turned out to be faster for lower support thresholds. To back up our claims, we present test results based on real-life datasets.