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首页> 外文期刊>International Journal of Innovative Computing Information and Control >HYBRID ASSOCIATIVE CLASSIFICATION ALGORITHM USING ANT COLONY OPTIMIZATION
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HYBRID ASSOCIATIVE CLASSIFICATION ALGORITHM USING ANT COLONY OPTIMIZATION

机译：蚁群优化的混合联想分类算法

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摘要

Classification rule discovery and association rules mining are two important data mining tasks. Association rules mining discovers all those rules from the training set that satisfies minimum support and confidence threshold while classification rule mining discovers a set of rules for predicting the class of unseen data. In this paper, we proposed a hybrid classification algorithm called ACO-AC, combining the idea of association rules mining and supervised classification using ant colony optimization. It is a class based association rules mining. The proposed technique integrates the classification with the association rule mining to discover high quality rules for improving the performance of classifier. Ant colony optimization is used to mine only the more appropriate subset of class association rules instead of exhaustively searching for all possible rules. First, strong association rules are discovered based on confidence and support and then, these rules are used to classify the unseen data. In proposed approach, we can mine association rules of each class parallel in distributed manner. We compared the proposed approach with eight other state of the art classification algorithms on twenty six data sets. Experiments results show that the hybrid classifier is more accurate and achieves higher accuracy rates when compared with other classification techniques.
机译：分类规则发现和关联规则挖掘是两个重要的数据挖掘任务。关联规则挖掘从训练集中发现所有满足最小支持和置信度阈值的规则，而分类规则挖掘则发现用于预测未见数据类别的一组规则。在本文中，我们提出了一种混合分类算法，称为ACO-AC，结合了关联规则挖掘和使用蚁群优化进行监督分类的思想。这是一个基于类的关联规则挖掘。所提出的技术将分类与关联规则挖掘相结合，以发现高质量规则以提高分类器的性能。蚁群优化用于仅挖掘类关联规则的更合适子集，而不是穷举搜索所有可能的规则。首先，基于置信度和支持度发现强关联规则，然后将这些规则用于分类未见数据。在提出的方法中，我们可以以分布式方式挖掘每个类别的关联规则。我们在26个数据集上将提出的方法与其他八种最新的分类算法进行了比较。实验结果表明，与其他分类技术相比，混合分类器更准确，准确率更高。

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