TY - JFULL AU - Amdouni Hamida and Gammoudi Mohamed Mohsen PY - 2015/9/ TI - Semi-Automatic Method to Assist Expert for Association Rules Validation T2 - International Journal of Computer and Information Engineering SP - 1918 EP - 1927 VL - 9 SN - 1307-6892 UR - https://publications.waset.org/pdf/10002201 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 104, 2015 N2 - In order to help the expert to validate association rules extracted from data, some quality measures are proposed in the literature. We distinguish two categories: objective and subjective measures. The first one depends on a fixed threshold and on data quality from which the rules are extracted. The second one consists on providing to the expert some tools in the objective to explore and visualize rules during the evaluation step. However, the number of extracted rules to validate remains high. Thus, the manually mining rules task is very hard. To solve this problem, we propose, in this paper, a semi-automatic method to assist the expert during the association rule's validation. Our method uses rule-based classification as follow: (i) We transform association rules into classification rules (classifiers), (ii) We use the generated classifiers for data classification. (iii) We visualize association rules with their quality classification to give an idea to the expert and to assist him during validation process. ER -