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.

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.

通常，在分类中，您有一组预定义的类，并希望知道新对象属于哪个类。

聚类尝试将一组对象分组，并发现对象之间是否存在某种关系。

在机器学习的背景下，分类是监督学习，聚类是无监督学习。

也可以看看维基百科上的分类和聚类。

聚类是一种对对象进行分组的方法，通过这种方式，具有相似特征的对象聚集在一起，而具有不同特征的对象分开。它是机器学习和数据挖掘中常用的统计数据分析技术。

分类是在训练数据集的基础上识别、区分和理解对象的分类过程。分类是一种有监督的学习技术，其中训练集和正确定义的观察是可用的。

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。

There are two definitions in data mining "Supervised" and "Unsupervised". When someone tells the computer, algorithm, code, ... that this thing is like an apple and that thing is like an orange, this is supervised learning and using supervised learning (like tags for each sample in a data set) for classifying the data, you'll get classification. But on the other hand if you let the computer find out what is what and differentiate between features of the given data set, in fact learning unsupervised, for classifying the data set this would be called clustering. In this case data that are fed to the algorithm don't have tags and the algorithm should find out different classes.