无监督学习:使用不同的降维算法运行聚类算法并比较性能-源码

上传者: 42128558 | 上传时间: 2021-04-01 13:04:50 | 文件大小: 1.97MB | 文件类型: ZIP
无监督学习 概述 该存储库运行集群和降维技术。 运行的两种聚类算法是K均值和期望最大化。 运行的4维降维算法是主成分分析(PCA),独立成分分析(ICA),随机投影(RP)和递归特征消除(RFE)。 该存储库运行以下内容并捕获性能: 运行两种聚类算法 运行降维,然后进行聚类算法 降维和聚类算法的神经网络 数据集是来自UCI机器学习存储库的Adult和Wine数据集。 运行步骤 需要Python 3.6 从requirements.txt安装以下要求 使用python 3运行以下文件以创建数据文件 run_experiment.py UnSupervisedLearning_abalone.py UnSupervisedLearning_white_wine_quality.py 获得的结果 有关获得的结果的更多信息,请参考Analysis.pdf。

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