基于分类修剪的关联分类算法改进

针对现有关联分类算法资源消耗大、规则剪枝难、分类模型复杂的缺陷，提出了一种基于分类修剪的关联分类算法改进方案ACCP.根据分类属性值的不同对分类规则前项进行分块挖掘，并对频繁项集挖掘过程和规则修剪进行了改进，有效提高了分类准确率和算法运行效率.实验结果表明，此算法改进方案相比传统CBA算法和C4.5决策树算法有着更高的分类准确率，取得了较好的应用效果.

Improved Association Classification Algorithm Based on Classification Pruning

Aiming at the shortcomings of the existing association classification algorithm, such as large resource consumption, difficult rule pruning, and complex classification model, an improved classification scheme ACCP based on classification and pruning is proposed. The algorithm mines the fore items of classification rules respectively according to the different classification attribute values, and improves the frequent item set mining process and rule pruning, which effectively improves the classification accuracy and algorithm operation efficiency. The experimental results show that the improved algorithm has higher classification accuracy than traditional CBA algorithm and C4.5 decision tree algorithm, and has achieved satisfied application results.