Mining positive and negative association rules

Ramasubbareddy, B.; Govardhan, A.; Ramamohanreddy, A.

doi:10.1109/iccse.2010.5593755

Cited by 18 publications

(13 citation statements)

References 11 publications

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“…According to [25] the positive and negatives rules gives better classification accuracy thus helps in reasoning with less classification time. Moreover, [26] highlighted the importance of NAR besides PAR where better decision can be made especially in devising marketing strategies. Figure 1 describes the flowchart of the hybridized method.…”

Section: The Proposed Hybrid Rough_negative Algorithmmentioning

confidence: 99%

Outlier detection method based on hybrid rough

Ahmad

Shaari²,

Long

2014

Proceedings of the 8th International Conference on Ubiquitous Information Management and Communication

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This paper discusses on the detection of outliers by hybriding Rough_Outlier Algorithm with Negative Association Rules. An optimization algorithm named Binary Particle Swarm Optimization is used to improve the computation of Non_Reduct in order to detect outliers. By using Binary PSO algorithm, the rules generated from Rough_Outliers algorithm is optimized, giving significant outliers object detected. The detection of outliers process is then enhanced by hybriding it with Negative Association Rules. Frequent and Infrequent item sets from outlier rules are generated. Results show that the hybrid Rough_Negative algorithm is able to uncover meaningful knowledge of outliers from the frequent and infrequent item sets. These knowledge can then be used by experts in their field of domain for better decision making.

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Section: The Proposed Hybrid Rough_negative Algorithmmentioning

confidence: 99%

Outlier detection method based on hybrid rough

Ahmad

Shaari²,

Long

2014

Proceedings of the 8th International Conference on Ubiquitous Information Management and Communication

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“…Studies over NAR have shown that the rule is effective, efficient and promising algorithm [34,35]. NAR is also use to interpret meaningful knowledge from the rules of information systems [36,46].…”

Section: Negative Association Rulesmentioning

confidence: 99%

“…NAR is also use to interpret meaningful knowledge from the rules of information systems [36,46]. According to [34] mining PAR and NAR rules is useful for constructing associative classifiers following the supportconfidence framework as in Association Rules mining. By using the framework, no extra scan is required to mine NAR rules.…”

Section: Negative Association Rulesmentioning

confidence: 99%

Mining meaningful outlier using rough-negative association algorithm in heartdisease dataset

Ahmad

Shaari²,

Zalizah

2015

Proceedings of the 9th International Conference on Ubiquitous Information Management and Communication

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This paper discusses on finding meaningful outliers by hybridizing Rough_Outlier Algorithm with Negative Association Rules (NAR). By using NAR rules, it is expected that the rules generated will give a better perspective of outliers detection for medical diagnosis on Heartdisease dataset. To improve the computation of outliers, a Binary Particle Swarm Optimization algorithm is used. Only strong and interesting rules are generated from Positive and NAR rules, hence suitable measurements for support and confidence are used in the research work. Results showed that PAR and NAR rules are able to give better perspective on identifying outliers and in giving meaningful outliers for predictive medical analysis in case of Heartdisease. For further work in future, a test for accuracy will be applied by applying the false positive and true negative measurements. In conclusion this work is significant for giving different perspective of medical diagnosis of Heartdisease, hence assisting experts in their field for better decision making.

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“…In paper [4], they proposed Associative classification which is a classification of a new tuple using association rules. It is a combination of association rule mining and classification.…”

Section: Literature Reviewmentioning

confidence: 99%

Mining Association Rule in Classified Data for Course Recommender System in E-Learning

Aher¹,

L.M.R.J²

2012

IJCA

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The ADTree (Alternating Decision Tree) is a supervised classification technique that combines decision trees with the predictive accuracy into a set of classification rules & association rule algorithms are used to show the relationship between data items. Here in this paper we combine these two algorithms & apply it to sample data obtained from Moodle courses of our college for the Course Recommender System which predicts the course selected by the students. First we consider the result using only the association rule then we consider this combined approach. Here we present the advantage of applying the combined approach to Course Recommender System as compare to the result of applying only the association rule algorithm. We found that combined approach works better than only the association rule mining. This combined approach also increases the strength of the rules.

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Mining positive and negative association rules

Cited by 18 publications

References 11 publications

Outlier detection method based on hybrid rough

Outlier detection method based on hybrid rough

Mining meaningful outlier using rough-negative association algorithm in heartdisease dataset

Mining Association Rule in Classified Data for Course Recommender System in E-Learning

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