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A transaction mapping algorithm for frequent itemsets mining
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DOI：10.1109/TKDE.2006.1599386.png)
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Abstract
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In this paper, we present a novel algorithm for mining complete frequent itemsets. This algorithm is referred to as the TM ( Transaction Mapping) algorithm from hereon. In this algorithm, transaction ids of each itemset are mapped and compressed to continuous transaction intervals in a different space and the counting of itemsets is performed by intersecting these interval lists in a depth-first order along the lexicographic tree. When the compression coefficient becomes smaller than the average number of comparisons for intervals intersection at a certain level, the algorithm switches to transaction id intersection. We have evaluated the algorithm against two popular frequent itemset mining algorithms, FP-growth and dEclat, using a variety of data sets with short and long frequent patterns. Experimental data show that the TM algorithm outperforms these two algorithms.
Keywords:
algorithms
association rule mining
data mining
frequent itemsets
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