Combined method of genetic programming and association rule algorithm

Niimi, Ayahiko; Tazaki, Eiichiro

doi:10.1080/088395101753210764

Cited by 4 publications

(1 citation statement)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A hybrid model consisting of the Apriori algorithm and a constraint-based genetic algorithm (ACBGA) was designed for efficient classification of electronic ballast data [2]. In another approach, association rules generated by an Apriori miner are further refined by the aid of a genetic programming module [3,4]. Using support vector machines (SVM), a constructive method for extraction of association rules is proposed in which the classification knowledge is encoded in to an SVM classification tree (SVMT) and linguistic association rules are then decoded by using the trained SVMT [5].…”

Section: Introductionmentioning

confidence: 99%

Concept Classification Using a Hybrid Data Mining Model

Brown

Forouraghi

2009

2009 21st IEEE International Conference on Tools With Artificial Intelligence

View full text Add to dashboard Cite

Apriori is a well-known algorithm which is used extensively in market-basket analysis and data mining. The algorithm is used for learning association rules from transactional data bases and is based on simple counting procedures. In this paper we propose enhancements to Apriori which allow it to perform concept classification similar to the way decision tree algorithms learn. Specifically, training examples are modified and treated as transactional data and the results are verified and further improved by C4.5 decision tree and k-means clustering algorithms, respectively. To demonstrate the novelty of the enhanced Apriori algorithm, we present a hybrid data mining model (HDMM) which identifies atrisk students based on their academic performance and other pertinent data.

show abstract

Section: Introductionmentioning

confidence: 99%

Concept Classification Using a Hybrid Data Mining Model

Brown

Forouraghi

2009

2009 21st IEEE International Conference on Tools With Artificial Intelligence

View full text Add to dashboard Cite

show abstract

The hybrid of association rule algorithms and genetic algorithms for tree induction: an example of predicting the student course performance

Hsu

Lai

Chiu

2003

Expert Systems with Applications

View full text Add to dashboard Cite

Mining Three-Dimensional Anthropometric Body Surface Scanning Data for Hypertension Detection

Chiu

Hsu²,

Hsu³

et al. 2007

IEEE Trans. Inform. Technol. Biomed.

View full text Add to dashboard Cite

Hypertension is a major disease, being one of the top ten causes of death in Taiwan. The exploration of three-dimensional (3-D) anthropometry scanning data along with other existing subject medical profiles using data mining techniques becomes an important research issue for medical decision support. This research attempts to construct a prediction model for hypertension using anthropometric body surface scanning data. This research adopts classification trees to reveal the relationship between a subject's 3-D scanning data and hypertension disease using the hybrid of the association rule algorithm (ARA) and genetic algorithms (GAs) approach. The ARA is adopted to obtain useful clues based on which the GA is able to proceed its searching tasks in a more efficient way. The proposed approach was experimented and compared with a regular genetic algorithm in predicting a subject's hypertension disease. Better computational efficiency and more accurate prediction results from the proposed approach are demonstrated.

show abstract

Combined method of genetic programming and association rule algorithm

Cited by 4 publications

References 11 publications

Concept Classification Using a Hybrid Data Mining Model

Concept Classification Using a Hybrid Data Mining Model

The hybrid of association rule algorithms and genetic algorithms for tree induction: an example of predicting the student course performance

Mining Three-Dimensional Anthropometric Body Surface Scanning Data for Hypertension Detection

Contact Info

Product

Resources

About