October 2020
Intermediate to advanced
693 pages
16h 26m
Chinese
Content preview from 机器学习实战：基于Scikit-Learn、Keras 和TensorFlow （原书第2 版）
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212
｜
第
9
章
密度估算
这是估计生成数据集的随机过程的概率密度函数（PDF）的任务，密度估算通常用
于异常检测：位于非常低密度区域的实例很可能是异常。它对于数据分析和可视化
也很有用。
准备好蛋糕了吗？我们将从使用 K-Means 和 DBSCAN 进行聚类开始，然后讨论高斯混
合模型，并了解如何将它们用于密度估计、聚类和异常检测。
9.1 聚类
你在山中徒步旅行时，偶然发现了从未见过的植物。你环顾四周，发现还有很多。它们
并不完全相同，但是它们足够相似，你可能知道它们有可能属于同一物种（或至少属于
同一属）。你可能需要植物学家告诉你什么是物种，但你当然不需要专家来识别外观相
似的物体组。这称为聚类：识别相似实例并将其分配给相似实例的集群或组。
就像在分类中一样，每个实例都分配给一个组。但是与分类不同，聚类是一项无监督任
务。考虑图 9-1：左侧是鸢尾花数据集（在第 4 章中介绍），其中每个实例的种类（即类）
用不同的标记表示。它是一个标记的数据集，非常适合使用逻辑回归、SVM 或随机森
林分类器等分类算法。右侧是相同的数据集，但是没有标签，因此你不能再使用分类算
法。这就是聚类算法的引入之处，它们中的许多算法都可以轻松检测左下角的集群。肉
眼也很容易看到，但是右上角的集群由两个不同的子集群组成，并不是很明显。也就是
说，数据集具有两个附加特征（萼片长度和宽度），此处未表示，并且聚类算法可以很好
地利用所有的特征，因此实际上它们可以很好地识别三个聚类（例如，使用高斯混合模
型，在 150 个实例中，只有 5 ...
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