基于GA - ANT混合技术的原型优化分类

Mahtaab Khan, Deepika Dubey, Huma Gupta, A. Saxena
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引用次数: 1

摘要

学习是从海量数据中生成有用信息的过程。学习可以分为监督学习和无监督学习。分类是一种监督学习,聚类是一种无监督学习。原型聚类和分类是数据挖掘中的一项新技术。原型聚类和分类也称为集成聚类和分类。提出了一种基于蚁群优化和遗传算法的原型聚类与分类新方法。利用UCI存储库和三个数据集,对基于GA - ANT混合技术的原型聚类和分类进行了实验。实验结果表明,该方法比其他常用的数据挖掘方法具有更好的分类性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimize Prototype Classification Based on GA ANT Hybrid Technique
Learning is the process of generating useful information from a huge volume of data. Learning can be classified as supervised learning and unsupervised learning. Classification is a kind of supervised learning and Clustering is a kind of unsupervised learning. Prototype clustering and classification is new technique in data mining. The prototype clustering and classification also called as ensemble clustering and classification. The Proposed novel method for prototype clustering and classification based on ant colony optimization and genetic algorithm. We conduct experiments with the prototype clustering and classification based on GA ANT hybrid technique using UCI repository with three data sets. The experimental results show that the proposed approach can achieve superior classification performance than other commonly used data mining approaches.
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