Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach

By Jiawei Han, Jian Pei, Yiwen Yin, Runying Mao. Data mining and knowledge discovery, 2004

2,805 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Computer Networks and Communications, Information Systems

View Paper: https://www.semanticscholar.org/paper/06c4559125ddaa4371883541518eb6c4e736436d

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Citations in the corpus

Efficient Tree Structures for High Utility Pattern Mining in Incremental Databases (2009, background): “In the following sections, we will discuss research on frequent pattern mining, weighted frequent pattern mining, high utility pattern mining, and incremental and interactive pattern mining.”
Robustness in Coreference Resolution (2020, methodology): “3 Data Structure For representing the input samples, we use the Frequent Pattern Tree (FP-Tree) structure that is the data structure of the FP-Growth algorithm (Han et al., 2004), i.”
Fast algorithms for frequent itemset mining using FP-trees (2005, methodology): “Furthermore, when the FP-tree is too large to fit in memory, the current solutions need a very large number of disk I/Os for reading and writing FP-trees onto secondary memory or generating many intermediate databases [15], which makes mining frequent itemsets too time-consuming.”
Swarm (2010, background): “Note that even though our problem is defined in the similar form of frequent pattern mining [11], none of previous work in frequent pattern (FP) mining area can solve exactly our problem.”
A Fuzzy Association Rule-Based Classification Model for High-Dimensional Problems With Genetic Rule Selection and Lateral Tuning (2011, methodology): “4) Classiﬁcation based on multiple association rules (CMAR) [14]: This method extends an efﬁcient frequent pattern (FP) mining method, i.e., FP-Growth [63], constructs a class distribution-associated FP-tree, and mines large databases efﬁciently.”
FAST ALGORITHM FOR MINING ASSOCIATION RULES (2014, background): “The FP-growth algorithm [12] is the best–known instance of the second type.”
A Survey of Sequential Pattern Mining (2017, methodology): “The most popular pattern-growth algorithm for sequential pattern mining is PreﬁxSpan [89], which draws inspiration from the FPGrowth algorithm for itemset mining [53].”
PrivBasis: Frequent Itemset Mining with Differential Privacy (2012, methodology): “The two most prominent ones are the Apriori algorithm [5], and the FP-Growth algorithm [22].”
Isolated items discarding strategy for discovering high utility itemsets (2008, methodology): “frequent itemsets, this experiment employed the FP-growth algorithm [ 16 ] to generate”
Mining high utility itemsets without candidate generation (2012, methodology): “The algorithms based on the FP-Growth algorithm [9] show better performance.”
Explainable and Interpretable Models in Computer Vision and Machine Learning (2017, background): “, all rules that satisfy a minimum support and minimum confidence threshold are found (Agrawal et al. 1995; Zaki et al. 1997; Han et al. 2004), and subsequently filtered and/or ordered according to heuristics.”
SPMF: a Java open-source pattern mining library (2014, methodology): “However, the source code of Coron is not public, Illimine provides the source code of only one of its pattern mining algorithms and LUCS-KDD source code cannot be used for commercial purposes.”
Hierarchical graph embedding in vector space by graph pyramid (2017, background): “In pattern extraction, the summaries are a set of frequent subgraphs [25].”
Survey on Frequent Pattern Mining (2003, neutral): “This new data structure is denoted by FP-tree (FrequentPattern tree) and is created as follows [20].”