کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
387716 | 660906 | 2012 | 9 صفحه PDF | دانلود رایگان |

Association rule mining and classification are important tasks in data mining. Using association rules has proved to be a good approach for classification. In this paper, we propose an accurate classifier based on class association rules (CARs), called CAR-IC, which introduces a new pruning strategy for mining CARs, which allows building specific rules with high confidence. Moreover, we propose and prove three propositions that support the use of a confidence threshold for computing rules that avoids ambiguity at the classification stage. This paper also presents a new way for ordering the set of CARs based on rule size and confidence. Finally, we define a new coverage strategy, which reduces the number of non-covered unseen-transactions during the classification stage. Results over several datasets show that CAR-IC beats the best classifiers based on CARs reported in the literature.
► Advantages of specific rules with high confidence in classification based on CARs.
► Avoid ambiguity at the classification stage may increase the classification accuracy.
► A new coverage strategy, during the classification stage is proposed.
Journal: Expert Systems with Applications - Volume 39, Issue 12, 15 September 2012, Pages 11203–11211