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Abstract
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| Frequent itemset mining is today one of the most popular data mining techniques. Its application is, how- ever, hindered by the high computational cost in many real-world datasets, especially for smaller values of support thresholds. In many cases, moreover, the large number of frequent itemsets discovered is over- whelming. In some real-world applications, it is sufficient to find a smaller subset of frequent itemsets, such as identifying the frequent itemsets with a maximum length. In this paper, we present a prun- ing algorithm, called LengthSort, that reduces the search space effectively and improves the efficiency of mining frequent itemsets with a maximum length. LengthSort prunes both the items and the transactions before constructing a Frequent Pattern tree structure. Our experiments on several datasets show that the proposed pruning techniques reduce the time needed to discover the frequent itemsets with a maximum length. The proposed pruning algorithm can also be applied to efficiently discover frequent itemsets that are longer than a user-specified threshold. | |
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International
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Si |
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JCR
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Si |
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Title
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Expert Systems With Applications |
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ISBN
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0957-4174 |
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Impact factor JCR
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Impact info
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Volume
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142 |
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10.1016/j.eswa.2019.113004 |
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Journal number
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From page
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113004 |
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To page
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113027 |
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Month
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SIN MES |
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Ranking
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