کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
383475 660823 2012 10 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Classification based on association rules: A lattice-based approach
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Classification based on association rules: A lattice-based approach
چکیده انگلیسی

Classification plays an important role in decision support systems. A lot of methods for mining classification rules have been developed in recent years, such as C4.5 and ILA. These methods are, however, based on heuristics and greedy approaches to generate rule sets that are either too general or too overfitting for a given dataset. They thus often yield high error ratios. Recently, a new method for classification from data mining, called the Classification Based on Associations (CBA), has been proposed for mining class-association rules (CARs). This method has more advantages than the heuristic and greedy methods in that the former could easily remove noise, and the accuracy is thus higher. It can additionally generate a rule set that is more complete than C4.5 and ILA. One of the weaknesses of mining CARs is that it consumes more time than C4.5 and ILA because it has to check its generated rule with the set of the other rules. We thus propose an efficient pruning approach to build a classifier quickly. Firstly, we design a lattice structure and propose an algorithm for fast mining CARs using this lattice. Secondly, we develop some theorems and propose an algorithm for pruning redundant rules quickly based on these theorems. Experimental results also show that the proposed approach is more efficient than those used previously.


► A new structure called lattice of class rules is proposed for mining class-association rules (CARs) efficiently.
► An algorithm for mining CARs based on the structure is designed.
► Theorems for pruning redundant rules are proven and pruning strategies are developed.
► Experimental results show the good performance of the proposed approach.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Expert Systems with Applications - Volume 39, Issue 13, 1 October 2012, Pages 11357–11366
نویسندگان
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