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Issue No.07 - July (2004 vol.16)
pp: 875-879
Wee-Keong Ng , IEEE Computer Society
ABSTRACT
<p><b>Abstract</b>—The importance of data mining is apparent with the advent of powerful data collection and storage tools; raw data is so abundant that manual analysis is no longer possible. Unfortunately, data mining problems are difficult to solve and this prompted the introduction of several novel data structures to improve mining efficiency. Here, we will critically examine existing preprocessing data structures used in association rule mining for enhancing performance in an attempt to understand their strengths and weaknesses. Our analyses culminate in a practical structure called the <it>SOTrieIT</it> (Support-Ordered Trie Itemset) and two synergistic algorithms to accompany it for the fast discovery of frequent itemsets. Experiments involving a wide range of synthetic data sets reveal that its algorithms outperform <it>FP-growth</it>, a recent association rule mining algorithm with excellent performance, by up to two orders of magnitude and, thus, verifying its efficiency and viability.</p>
INDEX TERMS
Data mining, association rule mining, data structures.
CITATION
Yew-Kwong Woon, Wee-Keong Ng, Ee-Peng Lim, "A Support-Ordered Trie for Fast Frequent Itemset Discovery", IEEE Transactions on Knowledge & Data Engineering, vol.16, no. 7, pp. 875-879, July 2004, doi:10.1109/TKDE.2004.1318569
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