TBAR: An efficient method for association rule mining in relational databases

Berzal, Fernando; Cubero, Juan-Carlos; Marín, Nicolás; Serrano, José María González

doi:10.1016/s0169-023x(00)00055-0

Cited by 87 publications

(43 citation statements)

References 14 publications

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“…Our program accesses data stored in relational databases through JDBC (which stands for 'Java Database Connectivity', the standard call-level interface of the Java programming language). Our implementation of ART makes use of TBAR (Berzal et al, 2001), an Apriori-like algorithm for finding frequent itemsets (Agrawal & Srikant, 1994). We also used AspectJ, an aspect-oriented extension of the Java programming language (Kiczales et al, 2001), in order to monitor I/O operations in our experiments.…”

Section: Resultsmentioning

confidence: 99%

“…Many algorithms have been proposed to solve this problem, from the original proposals, such as AIS (Agrawal, Imielinski, & Swami, 1993), SETM (Houtsma & Swami, 1993), and, in particular, Apriori (Agrawal & Srikant, 1994), to more advanced algorithms such as DHP (Park, Chen, & Yu, 1995), DIC (Brin et al, 1997), CARMA (Hidber, 1999), FP-Growth (Han, Pei, & Yin, 2000), and TBAR (Berzal et al, 2001). See Hipp, Güntzer, and Nakhaeizadeh (2000) for a recent survey of the problem and Han and Plank (1996) for a somewhat older comparison of some selected algorithms.…”

Section: Association Rulesmentioning

confidence: 99%

“…Since dense datasets are common in the relational databases typically used for classification problems (i.e., datasets with a relatively small number of missing values), an algorithm like TBAR is the best choice (Berzal et al, 2001) because it takes advantage of the First Normal Form to reduce the size of the candidate set in the association rule mining process (i.e., the number of potentially relevant itemsets). 1 The MinSupp parameter can be a fixed number of tuples, or a percentage of the size of the current training dataset.…”

Section: Building the Classifiermentioning

confidence: 99%

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ART: A Hybrid Classification Model

et al. 2004

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Abstract. This paper presents a new family of decision list induction algorithms based on ideas from the association rule mining context. ART, which stands for 'Association Rule Tree', builds decision lists that can be viewed as degenerate, polythetic decision trees. Our method is a generalized "Separate and Conquer" algorithm suitable for Data Mining applications because it makes use of efficient and scalable association rule mining techniques.

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Section: Resultsmentioning

confidence: 99%

Section: Association Rulesmentioning

confidence: 99%

Section: Building the Classifiermentioning

confidence: 99%

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ART: A Hybrid Classification Model

et al. 2004

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“…There are many algorith ms presented which are based on Apriori algorithm [9][10][11][12]. The efficiency of algorith m is based on their imp lementation.…”

Section: Introductionmentioning

confidence: 99%

Comparison of New Multilevel Association Rule Algorithm with MAFIA

Shrivastava¹,

Jain²,

Shrivastava³

2014

IJISA

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Abstract-M ultilevel association rules provide the more precise and specific information. Apriori algorithm is an established algorithm for finding association rules. Fast Apriori implementation is modified to develop new algorithm for finding frequent item sets and mining multilevel association rules. M AFIA is another established algorithm for finding frequent item sets. In this paper, the performance of this new algorithm is analyzed and compared with M AFIA algorithm.

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“…Accordingly, research on association rule mining usually focuses on establishing efficient methods to identify all frequent itemsets. Numerous efficient methods have been proposed to discover frequent itemsets, such as level-wise algorithms [2,3,7,8,12,30] and pattern-growth methods [1,15,16,21,26].…”

Section: Introductionmentioning

confidence: 99%

Isolated items discarding strategy for discovering high utility itemsets

Yeh

Chang

2008

Data & Knowledge Engineering

248

139

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Traditional methods of association rule mining consider the appearance of an item in a transaction, whether or not it is purchased, as a binary variable. However, customers may purchase more than one of the same item, and the unit cost may vary among items. Utility mining, a generalized form of the share mining model, attempts to overcome this problem. Since the Apriori pruning strategy cannot identify high utility itemsets, developing an efficient algorithm is crucial for utility mining. This study proposes the Isolated Items Discarding Strategy (IIDS), which can be applied to any existing level-wise utility mining method to reduce candidates and to improve performance. The most efficient known models for share mining are ShFSM and DCG, which also work adequately for utility mining as well. By applying IIDS to ShFSM and DCG, the two methods FUM and DCG+ were implemented, respectively. For both synthetic and real datasets, experimental results reveal that the performance of FUM and DCG+ is more efficient than that of ShFSM and DCG, respectively. Therefore, IIDS is an effective strategy for utility mining.

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TBAR: An efficient method for association rule mining in relational databases

Cited by 87 publications

References 14 publications

ART: A Hybrid Classification Model

ART: A Hybrid Classification Model

Comparison of New Multilevel Association Rule Algorithm with MAFIA

Isolated items discarding strategy for discovering high utility itemsets

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