MMAC: A New Multi-Class, Multi-Label Associative Classification Approach

Thabtah, Fadi; Cowling, Peter I.; Peng, Yonghong

doi:10.1109/icdm.2004.10117

Cited by 150 publications

(124 citation statements)

References 4 publications

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“…Otherwise, l i is marked and its support satisfies S L . Then, information of Obidset, count, and pos of all child nodes of l i is updated (lines [19][20][21][22][23][24][25][26][27]. If the support of O satisfies S L , then it is marked (lines 28 and 29).…”

Section: B) Modified Car-miner Algorithmmentioning

confidence: 99%

“…The class with the highest weighted χ2 is selected and assigned to this record. [20] proposed the MMAC method. MMAC uses multiple labels for each rule and multiple classes for prediction.…”

Section: Mining Class Association Rulesmentioning

confidence: 99%

“…Nguyen et al [17] proposed a parallel algorithm for fast mining CARs. Several methods for pruning and sorting rules have also been proposed [10,12,15,20,23,27]. These approaches are used for batch processing only; i.e., they are executed on the integration of original and inserted datasets.…”

Section: Mining Class Association Rulesmentioning

confidence: 99%

“…After that, a large number of methods related to this problem, such as Classification based on Multiple Association Rules (CMAR) [10], Classification based on Predictive Association Rules (CPAR) [27], Multi-class, Multi-label Associative Classification (MMAC) [20], ECR-CARM [23], and CAR-Miner [16] have been proposed. Results of classification based on association rule mining are often more accurate than those obtained based on ILA and decision tree [9,12,22].…”

Section: Introductionmentioning

confidence: 99%

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Updating mined class association rules for record insertion

Nguyen

2014

Appl Intell

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Mining class association rules is an interesting problem in classification and prediction. Some recent studies have shown that using classifiers based on class association rules resulted in higher accuracy than those obtained by using other classification algorithms such as C4.5 and ILA. Although many algorithms have been proposed for mining class association rules, they were used for batch processing. However, real-world datasets regularly change; thus, updating a set of rules is challenging. This paper proposes an incremental method for mining class association rules when records are inserted into the dataset. Firstly, a modified equivalence class rules tree (MECR-tree) is created from the original dataset. When records are inserted, nodes on the tree are updated by changing their information including Obidset (a set of object identifiers containing the node's itemset), count, and pos. Secondly, the concept of pre-large itemsets is applied to avoid re-scanning the original dataset. Finally, a theorem is proposed to quickly prune nodes that cannot generate rules in the tree update process. Experimental results show that the proposed method is more effective than mining entire original and inserted datasets.

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Section: B) Modified Car-miner Algorithmmentioning

confidence: 99%

Section: Mining Class Association Rulesmentioning

confidence: 99%

Section: Mining Class Association Rulesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Updating mined class association rules for record insertion

Nguyen

2014

Appl Intell

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“…A multi-label learning algorithm based on class association rules is proposed in [41]. The algorithm, named multi-class multi-label associative classification (MMAC), is divided into three modules: rules generation, recursive learning and classification.…”

Section: Other Techniquesmentioning

confidence: 99%

A Tutorial on Multi-label Classification Techniques

Carvalho

Freitas

2009

Studies in Computational Intelligence

105

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I-prune: Item selection for associative classification

Baralis

Garza

2012

Int. J. Intell. Syst.

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Associative classification is characterized by accurate models and high model generation time. Most time is spent in extracting and postprocessing a large set of irrelevant rules, which are eventually pruned. We propose I-prune, an item-pruning approach that selects uninteresting items by means of an interestingness measure and prunes them as soon as they are detected. Thus, the number of extracted rules is reduced and model generation time decreases correspondingly. A wide set of experiments on real and synthetic data sets has been performed to evaluate I-prune and select the appropriate interestingness measure. The experimental results show that I-prune allows a significant reduction in model generation time, while increasing (or at worst preserving) model accuracy. Experimental evaluation also points to the chi-square measure as the most effective interestingness measure for item pruning. C 2012 Wiley Periodicals, Inc.

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MMAC: A New Multi-Class, Multi-Label Associative Classification Approach

Cited by 150 publications

References 4 publications

Updating mined class association rules for record insertion

Updating mined class association rules for record insertion

A Tutorial on Multi-label Classification Techniques

I-prune: Item selection for associative classification

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