A Tree Projection Algorithm for Generation of Frequent Item Sets

By Ramesh C. Agarwal, Charu C. Aggarwal, V. V. V. Prasad. J. Parallel Distributed Comput., 2001

706 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/0d1ecb48d54a8e0ec1612b21f5efa6f0c5f14c5b

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Citations in the corpus

Sequential pattern mining -- approaches and algorithms (2013, methodology): “Guralnik and Karypis [2004] developed two static distribution algorithms, based on the tree projection algorithm for frequent itemset discovery [Agrawal et al. 1999], to take advantage of either data- or task-parallelism.”
Hierarchical Document Clustering using Frequent Itemsets (2003, background): “Many variations of the Apriori and frequent itemset extraction algorithm have been proposed [25, 23, 2, 3] to address these weaknesses.”
Frequent pattern mining: current status and future directions (2007, methodology): “There are many alternatives and extensions to the FP-growth approach, including depth-ﬁrst generation of frequent itemsets by Agarwal et al. (2001); H-Mine , by Pei et al. (2001) which explores a hyper-structure mining of frequent patterns; building alternative trees; exploring top-down and…”
CLOSET: An Efficient Algorithm for Mining Frequent Closed Itemsets (2000, methodology): “The method explores the concepts of projected database [1, 6], as shown in Figure 1.”
Analysis of recommendation algorithms for e-commerce (2000, methodology): “Apriori [2], DHP [19], Tree Projection algorithms [1] and the FP-tree [13] algorithms are some of the well-known algorithms for nding association rules from databases.”
Survey on Frequent Pattern Mining (2003, methodology): “Later, several other depth-first algorithms have been proposed [1, 2, 20] of which the FP-growth algorithm by Han et al.”
Isolated items discarding strategy for discovering high utility itemsets (2008, neutral): “level-wise algorithms [2, 3, 7, 8, 12, 30] and pattern-growth methods [ 1 , 15, 16, 21, 26].”
MAFIA: a maximal frequent itemset algorithm (2005, methodology): “For more details on the DepthProject algorithm, please refer to the paper by Agarwal et al. [38] .”
Moment: maintaining closed frequent itemsets over a stream sliding window (2004, background): “The prefix tree [1] is often used for mining association rules on static data sets.”
Effective personalization based on association rule discovery from web usage data (2001, methodology): “Frequent Itemset Graph is an extension of the lexicographic tree used in the tree projection algorithm of [1].”
Business Intelligence: Data Mining and Optimization for Decision Making (2009, methodology): “The first contributions on association rules can be found in Agrawal (1993a,b) and Agrawal and Srikant (1994). Surveys are presented in Zaki (1999) and Hipp et al. (2000). Considerable research effort has been devoted to the efficient generation of rules for massive datasets using parallel algorithm”
A survey of frequent subgraph mining algorithms (2012, methodology): “These two categories are similar in spirit to counterparts found in Association Rule Mining (ARM), namely the Apriori algorithm (Agrawal & Srikant 1994) and FP-growth algorithm (Han et al. 2000) respectively.”
An efficient algorithm for discovering frequent subgraphs (2004, methodology): “This is because the transaction framework that is assumed by these algorithms cannot be used to effectively model the data sets in these domains.”
Mining frequent patterns without candidate generation (2000, methodology): “Based on the techniques reported in [2], we implemented a memory-based version of TreeProjection.”
Association mining (2006, background): “Opportunistic Projection [Liu et al. 2002] is a hybrid pattern growth algorithm that extends the authors prior work, namely FP-Growth and H-Mine.”