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.

通过聚类，可以用所需的属性(如数量、形状和提取的聚类的其他属性)对数据进行分组。而在分类中，组的数量和形状是固定的。 大多数聚类算法都给出了聚类个数作为参数。然而，有一些方法可以找出合适的集群数量。

如果你试图将大量的文件归档到你的书架上(根据日期或文件的其他规格)，你是在分类。

如果要从这组工作表创建集群，则意味着工作表之间有一些类似的东西。

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

分类一行:

将数据分类为预定义的类别

用于集群的一行代码:

将数据分组到一组类别中

关键的区别:

分类是获取数据并将其放入预定义的类别中，而在聚类中，您想将数据分组到的类别集是事先不知道的。

结论:

Classification assigns the category to 1 new item, based on already labeled items while Clustering takes a bunch of unlabeled items and divide them into the categories In Classification, the categories\groups to be divided are known beforehand while in Clustering, the categories\groups to be divided are unknown beforehand In Classification, there are 2 phases – Training phase and then the test phase while in Clustering, there is only 1 phase – dividing of training data in clusters Classification is Supervised Learning while Clustering is Unsupervised Learning

我写了一篇关于同一主题的长文章，你可以在这里找到:

https://neelbhatt40.wordpress.com/2017/11/21/classification-and-clustering-machine-learning-interview-questions-answers-part-i/