Efficiently Using Prefix-trees in Mining Frequent Itemsets

By Gösta Grahne, Jianfei Zhu. Workshop on Frequent Itemset Mining Implementations, 2003

632 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/99e3735b2152e13543ae5ed1480daa7e87ea1b1d

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Jiawei Han
2. Hong Cheng
3. Jian Pei
4. Jaroslav M. Fowkes
5. Xifeng Yan
6. Dong Xin
7. Tao Xie
8. Mohammed J. Zaki

Citations in the corpus

• The PARSEC benchmark suite: Characterization and architectural implications (2008, methodology): “freqmine This application employs an array-based version of the FP-growth (Frequent Pattern-growth) method [10] for Frequent Itemset Mining (FIMI).”
• Mining API patterns as partial orders from source code: from usage scenarios to specifications (2007, background): “PR-Miner developed by Li and Zhou [15] uses frequent item set mining [10] to extract implicit programming rules from source code and detect their violations for detecting bugs.”
• Learning to rank code examples for code search engines (2017, methodology): “To identify the underlying usage pattern of a code example, we extract the method call sequence for each code snippet in the corpus and use the frequent itemset mining technique (Grahne and Zhu 2003) to identify the usage patterns in the corpus by analyzing the method calls that are frequently used ”
• Association mining (2006, methodology): “FPMax [Grahne and Zhu 2003b] provides an MFI version of FP-Growth which uses superset checking to construct an auxiliary MFI-Tree.”
• PR-Miner: automatically extracting implicit programming rules and detecting violations in large software code (2005, methodology): “Due to space limitation, we do not describe the details of the FPclose algorithm since they can be found in [14].”
• Discriminative Frequent Pattern Analysis for Effective Classification (2007, methodology): “We use FPClose [9] to generate closed patterns and MMRFS algorithm to do the feature selection.”
• Frequent pattern mining: current status and future directions (2007, methodology): “…mining of frequent patterns; building alternative trees; exploring top-down and bottom-up traversal of such trees in pattern-growth mining by Liu et al. (2002; 2003); and an array-based implementation of prefix-tree-structure for efficient pattern growth mining by Grahne and Zhu (2003).”
• LCM ver. 2: Efficient Mining Algorithms for Frequent/Closed/Maximal Itemsets (2004, methodology): “The performance of LCM algorithms are compared with the algorithms which marked good score on FIMI 03: fpgrowth[8], afopt[11], MAFIA[5, 6], kDCI[12], and PATRICIAMINE[16].”
• Benchmarking modern multiprocessors (2011, methodology): “The freqmine application employs an array-based version of the FP-growth (Frequent Pattern-growth) method [30] for Frequent Itemset Mining (FIMI).”