基于熵权的全局记忆LF蚁群聚类算法

针对LF蚁群聚类算法没有区分数据集属性重要度、算法效率低和聚类效果不稳定的问题，提出一种基于熵权的全局记忆LF算法(weighted global ant colony optimization, WGACO)。该算法首先通过熵权法计算各属性熵权，修改欧氏距离计算公式，以提升聚类精度；使用权重最大的属性值对数据对象进行初始化，增强聚类效果的稳定性；引入全局记忆矩阵减少蚂蚁的无效移动，提升算法效率；加入算法的收敛条件，提升算法实用性。选取UCI数据库中的7个真实数据集和3个人工生成的数据集进行数值实验，并与GMACO、SMACC、ILFACC三种改进LF的算法进行比较，实验结果表明，所提算法在精度、算法效率和稳定性上都有比较好的提升，在处理高维数据上也有较好的表现。最后，WGACO在商场会员用户细分上表现良好，体现了其实用价值。

Global memory LF ant colony clustering algorithm based on entropy weight

Aiming at the problem that LF ant colony clustering algorithm does not distinguish the importance of data set attributes, the algorithm efficiency is low and the clustering effect is unstable, this paper proposed a weighted global memory ant colony optimization(WGACO) based on entropy weight. Firstly, it calculated the entropy weight of each attribute by entropy weight method, and modified the Euclidean distance calculation formula to improve the clustering accuracy. It initialized the data object with the value of the largest weight attribute to enhance the stability of the clustering effect, and introduced the global memory matrix to reduce the invalid movement of ants and improve the efficiency of the algorithm. It added the convergence conditions of the algorithm to improve the practicability of the algorithm. Selecting seven real data sets and three artificially generated data sets in UCI database for numerical experiments, and compared WGACO with GMACO, SMACC and ILFACC three improved LF algorithms. The experimental results show that WGACO has a relatively good improvement in accuracy, algorithm efficiency and stability, and also has a good performance in processing high-dimensional data. Finally, WGACO has performed well in the segmentation of mall member users, reflecting its practical value.