Ants Algorithm for Mining Frequent Itemsets in Larger Database

HUANG Hong-xing

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Ants Algorithm for Mining Frequent Itemsets in Larger Database

更新时间：2026-09-04

- Ants Algorithm for Mining Frequent Itemsets in Larger Database
- Vol. 31, Issue 12, Pages: 144-147(2014)
- 作者机构：
  
  福建农林大学机构计算机与信息学院,福建,福州,350002
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2014
- 稿件说明：
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黄红星. 挖掘完全频繁项集的蚁群算法[J]. 微电子学与计算机, 2014, 31(12): 144-147,151.

HUANG Hong-xing. Ants Algorithm for Mining Frequent Itemsets in Larger Database[J]. 2014, 31(12): 144-147.
黄红星. 挖掘完全频繁项集的蚁群算法[J]. 微电子学与计算机, 2014, 31(12): 144-147,151. DOI：

HUANG Hong-xing. Ants Algorithm for Mining Frequent Itemsets in Larger Database[J]. 2014, 31(12): 144-147. DOI：

摘要

关联规则是数据挖掘发现的重要知识

完全频繁项集的发现是挖掘关联规则的关键步骤.蚁群算法是一种元启发式算法

已经有效应用于许多组合优化问题.因此

提出一种新的应用蚁群算法挖掘完全频繁项集的方法.对比实验表明

该算法是智能高效的.

Abstract

In the field of data mining

an important issue for association rules generation is frequent itemsets discovery

which is the key factor in implementing association rule mining.The ant colony optimization is one of the meta-heuristics for combinatorial optimization problems.Therefore

a novel approach of applying ant colony optimization for mining frequent itemsets is proposed.Compared experiments show that this algorithm is intelligent and effective.

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Related Author

DU Lin

CHEN Yun-liang

ZHU Jing

Related Institution

1 Computer Science Department, China University of Geosciences

2 Wuhan National Laboratory for Optoelectronics, Huazhong University of Science & Technology

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