TY  - JOUR
N2  - Associative classification is recognized by its high accuracy and strong flexibility in managing unstructured data. However, the performance is still induced by low quality dataset which comprises of noised and distorted data during data collection. The noisy data affected support value of an itemset and so it influenced the performance of an associative classification. The performance of associative classification is relied on the classification where the classification is worked based on the class association rules which generated from frequent rule mining process. To optimize the frequent itemsets based on the support value, in this research, we proposed a new optimization pruning technique to prune decision tree according to the correlation of each decision tree branches using genetic algorithm. © 2018, Springer International Publishing AG.
ID  - scholars10947
KW  - Association rules; Computational methods; Data acquisition; Decision trees; Genetic algorithms; Optimization; Trees (mathematics)
KW  -  Association rules mining; Associative classification; Class association rules; Data collection; Pruning; Pruning techniques; Support value; Unstructured data
KW  -  Data mining
Y1  - 2018///
JF  - Advances in Intelligent Systems and Computing
A1  - Chern-Tong, H.
A1  - Aziz, I.A.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85029598698&doi=10.1007%2f978-3-319-67621-0_18&partnerID=40&md5=68d8481b92f9b67593940bb6869e206e
VL  - 662
AV  - none
N1  - cited By 2; Conference of International Conference on Computational Methods in Systems and Software, CoMeSySo 2017 ; Conference Date: 12 September 2017 Through 14 September 2017; Conference Code:197849
SP  - 195
TI  - A Performance Evaluation of Chi-Square Pruning Techniques in Class Association Rules Optimization
PB  - Springer Verlag
SN  - 21945357
EP  - 203
ER  -