An efficient frequent pattern mining algorithm using a highly compressed prefix tree
The identification of frequent patterns plays a key role in mining association rules. FP-growth is a fundamental algorithm for frequent pattern mining. It employs a prefix tree structure (FP-Tree) and a recursive mining process to discover frequent patterns. However, the performance of FP-growth is...
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Veröffentlicht in: | Intelligent data analysis 2019-01, Vol.23 (S1), p.153-173 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | The identification of frequent patterns plays a key role in mining association rules. FP-growth is a fundamental algorithm for frequent pattern mining. It employs a prefix tree structure (FP-Tree) and a recursive mining process to discover frequent patterns. However, the performance of FP-growth is closely related to the total number of recursive calls, which leads to poor performance when multiple conditional FP-trees are required to be constructed. This paper proposes highly compressed FP-tree (HCFP-tree). This increases prefix sharing and reduces the number of nodes in the prefix tree. Based on HCFP-tree, we design a new algorithm called HCFP-growth. This algorithm greatly reduces the number of recursive calls required to mine full frequent patterns. Experiments conducted on various types of datasets demonstrate that HCFP-growth is always among the fastest algorithms. It also consumes the least memory in many cases, and its memory consumption is comparable to that of existing algorithms in other cases. |
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ISSN: | 1088-467X 1571-4128 |
DOI: | 10.3233/IDA-192645 |