分类和聚类之间的主要区别是: 分类是借助类标签对数据进行分类的过程。另一方面，聚类类似于分类，但没有预定义的类标签。 分类与监督学习相适应。与此相反，聚类也被称为无监督学习。 分类方法提供训练样本，聚类方法不提供训练数据。

希望这对你有所帮助!

如果你问过任何数据挖掘或机器学习的人这个问题，他们会使用术语监督学习和无监督学习来解释聚类和分类之间的区别。首先让我解释一下有监督和无监督这两个关键词。

Supervised learning: suppose you have a basket and it is filled with some fresh fruits and your task is to arrange the same type fruits at one place. suppose the fruits are apple,banana,cherry, and grape. so you already know from your previous work that, the shape of each and every fruit so it is easy to arrange the same type of fruits at one place. here your previous work is called as trained data in data mining. so you already learn the things from your trained data, This is because of you have a response variable which says you that if some fruit have so and so features it is grape, like that for each and every fruit.

这种类型的数据将从经过训练的数据中获得。 这种类型的学习被称为监督学习。 这种类型的解决问题属于分类。 所以你已经学会了这些东西，所以你可以自信地工作。

无监督: 假设你有一个篮子，里面装满了一些新鲜的水果，你的任务是把相同类型的水果摆放在一个地方。

这一次你对这些水果一无所知，你是第一次看到这些水果，所以你会如何安排相同类型的水果。

你首先要做的是拿起这个水果然后选择这个水果的任何物理特性。假设你取了颜色。

然后你会根据颜色来排列它们，然后这些组会是这样的。 红色组:苹果和樱桃水果。 绿色组:香蕉和葡萄。 那么现在你将用另一个物理字符作为大小，所以现在群是这样的。 红色和大尺寸:苹果。 红色，体积小，樱桃果状。 绿色，大个头:香蕉。 绿色，体积小，葡萄型。 工作完成了，大团圆结局。

这里你之前什么都没学，意味着没有训练数据和响应变量。 这种类型的学习被称为无监督学习。 聚类属于无监督学习。

聚类的目的是在数据中找到组。“集群”是一个直观的概念，确实如此 没有严格的数学定义。一个集群的成员应该是 彼此相似，而与其他集群的成员不同。一个集群 算法对一个未标记的数据集Z进行操作，并在其上生成一个分区。

对于类和类标签， 类包含相似的对象，而来自不同类的对象 是不同的。有些类具有明确的含义，在最简单的情况下 相互排斥。例如，在签名验证中，签名为任意一种 真的或伪造的。真正的阶级是两者之一，不管我们可能不是 能根据观察到的特定特征正确猜测的。

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。

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

I am sure a number of you have heard about machine learning. A dozen of you might even know what it is. And a couple of you might have worked with machine learning algorithms too.  You see where this is going? Not a lot of people are familiar with the technology that will be absolutely essential 5 years from now. Siri is machine learning. Amazon’s Alexa is machine learning. Ad and shopping item recommender systems are machine learning.  Let’s try to understand machine learning with a simple analogy of a 2 year old boy. Just for fun, let’s call him Kylo Ren

让我们假设凯洛·伦看到了一头大象。他的大脑会告诉他什么?(记住，即使他是维德的继任者，他也只有最低限度的思考能力)。他的大脑会告诉他，他看到了一个巨大的移动生物，颜色是灰色的。接着他看到一只猫，他的大脑告诉他那是一只会动的金色小动物。最后，他看到了一把光剑，他的大脑告诉他，这是一个无生命的物体，他可以玩!

此时他的大脑知道，军刀不同于大象和猫，因为军刀是用来玩的，不会自己移动。即使凯洛不知道移动是什么意思，他的大脑也能想出这么多。这个简单的现象叫做聚类。

机器学习只不过是这个过程的数学版本。 很多研究统计学的人意识到，他们可以用大脑工作的方式来计算一些方程。 大脑可以聚类相似的物体，大脑可以从错误中学习，大脑可以学习识别事物。

所有这些都可以用统计数据来表示，基于计算机模拟的这一过程被称为机器学习。为什么我们需要基于计算机的模拟?因为计算机比人脑更快地完成繁重的数学运算。 我很想进入机器学习的数学/统计部分，但在没有明确一些概念之前，你不会想直接进入。

Let’s get back to Kylo Ren. Let’s say Kylo picks up the saber and starts playing with it. He accidentally hits a stormtrooper and the stormtrooper gets injured. He doesn’t understand what’s going on and continues playing. Next he hits a cat and the cat gets injured. This time Kylo is sure he has done something bad, and tries to be somewhat careful. But given his bad saber skills, he hits the elephant and is absolutely sure that he is in trouble.  He becomes extremely careful thereafter, and only hits his dad on purpose as we saw in Force Awakens!!

从错误中学习的整个过程可以用方程式来模拟，在方程式中，做错事的感觉用错误或代价来表示。这种识别不该用军刀做什么的过程叫做分类。 聚类和分类是机器学习的绝对基础。让我们看看它们之间的区别。

Kylo differentiated between animals and light saber because his brain decided that light sabers cant move by themselves and are therefore, different. The decision was based solely upon the objects present (data) and no external help or advice was provided.  In contrast to this, Kylo differentiated the importance of being careful with light saber by first observing what hitting an object can do. The decision wasn’t completely based on the saber, but on what it could do to different objects . In short, there was some help here.

Because of this difference in learning, Clustering is called an unsupervised learning method and Classification is called a supervised learning method.  They are very different in the machine learning world, and are often dictated by the kind of data present. Obtaining labelled data (or things that help us learn , like stormtrooper,elephant and cat in Kylo’s case) is often not easy and becomes very complicated when the data to be differentiated is large. On the other hand, learning without labels can have it’s own disadvantages , like not knowing what are the label titles.  If Kylo was to learn being careful with the saber without any examples or help, he wouldn’t know what it would do. He would just know that it is not suppose to be done. It’s kind of a lame analogy but you get the point!

We are just getting started with Machine Learning. Classification itself can be classification of continuous numbers or classification of labels. For instance, if Kylo had to classify what each stormtrooper’s height is, there would be a lot of answers because the heights can be 5.0, 5.01, 5.011, etc. But a simple classification like types of light sabers (red,blue.green) would have very limited answers. Infact they can be represented with simple numbers. Red can be 0 , Blue can be 1 and Green can be 2.

如果你懂基础数学，你就知道0、1、2和5.1、5.01、5.011是不同的，分别被称为离散数和连续数。离散数的分类称为逻辑回归，连续数的分类称为回归。 逻辑回归也被称为分类分类，所以当你在其他地方读到这个术语时不要感到困惑

这是关于机器学习的一个非常基础的介绍。我将在下一篇文章中详细讨论统计方面的问题。如果我需要更正，请告诉我:)

第二部分张贴在这里。