TBAR: An efficient method for association rule mining in relational databases. In this paper, we propose a new algorithm for efficient association rule mining, which we apply in order to discover interesting patterns in relational databases. Our algorithm, which is called Tree-Based Association Rule mining (TBAR), redefines the notion of item and employs an effective tree data structure. It can also use techniques such as direct hashing and pruning. Experiments with real-life datasets show that TBAR outperforms Apriori, a well-known and widely used algorithm.

References in zbMATH (referenced in 7 articles , 1 standard article )

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  1. Mansingh, Gunjan; Osei-Bryson, Kweku-Muata; Reichgelt, Han: Using ontologies to facilitate post-processing of association rules by domain experts (2011) ioport
  2. Ayubi, Siyamand; Muyeba, Maybin K.; Baraani, Ahmad; Keane, John: An algorithm to mine general association rules from tabular data (2009) ioport
  3. Tsay, Yuh-Jiuan; Hsu, Tain-Jung; Yu, Jing-Rung: FIUT: A new method for mining frequent itemsets (2009) ioport
  4. Hsu, Ping-Yu; Chen, Yen-Liang; Ling, Chun-Ching: Algorithms for mining association rules in bag databases (2004)
  5. Martín-Bautista, M. J.; Sánchez, D.; Serrano, J. M.; Vila, M. A.: Text mining using fuzzy association rules (2004)
  6. Tsay, Yuh-Jiuan; Chang-Chien, Ya-Wen: An efficient cluster and decomposition algorithm for mining association rules. (2004) ioport
  7. Berzal, F.; Cubero, J.-C.; Marín, N.; Serrano, J.-M.: TBAR: An efficient method for association rule mining in relational databases (2001)