首先，像这里的许多回答一样:分类是有监督的学习，聚类是无监督的。这意味着:

Classification needs labeled data so the classifiers can be trained on this data, and after that start classifying new unseen data based on what he knows. Unsupervised learning like clustering does not uses labeled data, and what it actually does is to discover intrinsic structures in the data like groups. Another difference between both techniques (related to the previous one), is the fact that classification is a form of discrete regression problem where the output is a categorical dependent variable. Whereas clustering's output yields a set of subsets called groups. The way to evaluate these two models is also different for the same reason: in classification you often have to check for the precision and recall, things like overfitting and underfitting, etc. Those things will tell you how good is the model. But in clustering you usually need the vision of and expert to interpret what you find, because you don't know what type of structure you have (type of group or cluster). That's why clustering belongs to exploratory data analysis. Finally, i would say that applications are the main difference between both. Classification as the word says, is used to discriminate instances that belong to a class or another, for example a man or a woman, a cat or a dog, etc. Clustering is often used in the diagnosis of medical illness, discovery of patterns, etc.

我是一个数据挖掘的新手，但正如我的课本所说，分类应该是监督学习，而聚类应该是非监督学习。监督学习和无监督学习之间的区别可以在这里找到。

+分类: 给你一些新的数据，你必须为它们设置新的标签。

例如，一家公司希望对其潜在客户进行分类。当一个新客户来的时候，他们必须确定这个客户是否会购买他们的产品。

+集群: 你得到了一组历史交易记录，记录了谁买了什么。

通过使用聚类技术，您可以区分客户的细分。

分类——数据集可以有不同的组/类。红色，绿色和黑色。分类将试图找到将它们划分为不同类别的规则。

聚类——如果一个数据集没有任何类，而你想把它们放在某个类/分组中，你就可以进行聚类。上面紫色的圆圈。

如果分类规则不好，你就会在测试中出现错误分类，或者你的规则不够正确。 如果聚类不好，你会有很多异常值。不能落在任何集群中的数据点。

首先，像这里的许多回答一样:分类是有监督的学习，聚类是无监督的。这意味着:

Classification needs labeled data so the classifiers can be trained on this data, and after that start classifying new unseen data based on what he knows. Unsupervised learning like clustering does not uses labeled data, and what it actually does is to discover intrinsic structures in the data like groups. Another difference between both techniques (related to the previous one), is the fact that classification is a form of discrete regression problem where the output is a categorical dependent variable. Whereas clustering's output yields a set of subsets called groups. The way to evaluate these two models is also different for the same reason: in classification you often have to check for the precision and recall, things like overfitting and underfitting, etc. Those things will tell you how good is the model. But in clustering you usually need the vision of and expert to interpret what you find, because you don't know what type of structure you have (type of group or cluster). That's why clustering belongs to exploratory data analysis. Finally, i would say that applications are the main difference between both. Classification as the word says, is used to discriminate instances that belong to a class or another, for example a man or a woman, a cat or a dog, etc. Clustering is often used in the diagnosis of medical illness, discovery of patterns, etc.

我认为分类是将数据集中的记录分类为预定义的类，甚至是在运行中定义类。我认为这是任何有价值的数据挖掘的先决条件，我喜欢把它看作无监督学习，即在挖掘数据和分类作为一个很好的起点时，一个人不知道他/她在寻找什么

另一端的聚类属于监督学习，即一个人知道要寻找什么参数，它们之间的相关性以及关键水平。我认为这需要对统计学和数学有所了解