An associative classifier based on positive and negative rules

Antonie, Maria-Luiza; Zaı̈ane, Osmar R.

doi:10.1145/1008694.1008705

Cited by 81 publications

(49 citation statements)

References 7 publications

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“…1). The MECR-tree is generated with the lower minimum support threshold and then the safety threshold f is calculated using (1). A function of tree traversal for generating rules satisfying the upper minimum support threshold is then built.…”

Section: A Novel Methods For Updating Class Association Rules In Incrementioning

confidence: 99%

“…MMAC uses multiple labels for each rule and multiple classes for prediction. Antonie and Zaïane proposed an approach which uses both positive and negative rules to predict classes of new samples [1]. Vo and Le [23] presented the ECR-CARM algorithm for quickly mining CARs.…”

Section: Mining Class Association Rulesmentioning

confidence: 99%

“…If so, this node is added into the tree. Proof Because l 1 is a parent node of l 2 , it implies that l 1 .itemset ⊂ l 2 .itemset ⇒ all Obidsets containing l 1 .itemset also contain l 2 .itemset or l 1 . If the number of rows in dataset D' is not greater than f , then the algorithm simply updates the MECR-tree as follows.…”

Section: B) Modified Car-miner Algorithmmentioning

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: A Novel Methods For Updating Class Association Rules In Incrementioning

confidence: 99%

Section: Mining Class Association Rulesmentioning

confidence: 99%

Section: B) Modified Car-miner Algorithmmentioning

confidence: 99%

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Updating mined class association rules for record insertion

Nguyen

2014

Appl Intell

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“…Negation has been used in previous work in ARL. For example, Antonie and Zaïane [2] proposed an algorithm that discovers negative ARs (one example being, "if X exists in a record, then it is likely that Y will NOT exist". Baralis and Garza [4] constructed an associative classifier that used rules with negated words.…”

Section: Previous Workmentioning

confidence: 99%

Classification Inductive Rule Learning with Negated Features

Chua

Coenen

Malcolm

2010

Advanced Data Mining and Applications

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Abstract. This paper reports on an investigation to compare a number of strategies to include negated features within the process of Inductive Rule Learning (IRL). The emphasis is on generating the negation of features while rules are being "learnt"; rather than including (or deriving) the negation of all features as part of the input. Eight different strategies are considered based on the manipulation of three feature sub-spaces. Comparisons are also made with Associative Rule Learning (ARL) in the context of multi-class text classification. The results indicate that the option to include negated features within the IRL process produces more effective classifiers.

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“…of rules in a level is set to 30,000 in CARGBA. We have performed pruning using correlation coefficient introduced in [10].…”

Section: Experimental Studiesmentioning

confidence: 99%

A Novel Algorithm for Associative Classification

Kundu

Munir

Bari

et al.

Neural Information Processing

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Abstract. Associative classifiers have been the subject of intense research for the last few years. Experiments have shown that they generally result in higher accuracy than decision tree classifiers. In this paper, we introduce a novel algorithm for associative classification "Classification based on Association Rules Generated in a Bidirectional Apporach" (CARGBA). It generates rules in two steps. At first, it generates a set of high confidence rules of smaller length with support pruning and then augments this set with some high confidence rules of higher length with support below minimum support. Experiments on 6 datasets show that our approach achieves better accuracy than other state-of-the-art associative classification algorithms.

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An associative classifier based on positive and negative rules

Cited by 81 publications

References 7 publications

Updating mined class association rules for record insertion

Updating mined class association rules for record insertion

Classification Inductive Rule Learning with Negated Features

A Novel Algorithm for Associative Classification

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