blocks|key|994731|text|我是数据挖掘的新手，但正如我的课本上所说，CLASSICIATION应该是监督学习和聚类无监督学习。监督学习和无监督学习之间的区别可以在here上找到。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|994732|entityMap|0|LINK|mutability|MUTABLE|url|https://stackoverflow.com/questions/1832076/what-is-the-difference-between-supervised-learning-and-unsupervised-learning^0|1W|4|0|0^^$0|@$1|2|3|4|5|6|7|L|8|@]|9|@$A|M|B|N|1|O]]|C|$]]|$1|D|3|-4|5|6|7|P|8|@]|9|@]|C|$]]]|E|$F|$5|G|H|I|C|$J|K]]]]

I'm a new comer to Data Mining, but as my textbook says, CLASSICIATION is supposed to be supervised learning, and CLUSTERING unsupervised learning. The difference between supervised learning and unsupervised learning can be found <a href="https://stackoverflow.com/questions/1832076/what-is-the-difference-between-supervised-learning-and-unsupervised-learning">here</a>.

blocks|key|994817|text|%2B分类:给你一些新的数据，你必须为它们设置新的标签。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|994818|例如，一家公司希望对其潜在客户进行分类。当新客户到来时，他们必须确定这是不是要购买他们的产品的客户。|994819|%2B聚类:您将获得一组历史交易，其中记录了谁购买了什么。|994820|通过使用聚类技术，您可以区分客户的细分。|994821|entityMap^0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|J|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|K|8|@]|9|@]|A|$]]|$1|D|3|E|5|6|7|L|8|@]|9|@]|A|$]]|$1|F|3|G|5|6|7|M|8|@]|9|@]|A|$]]|$1|H|3|-4|5|6|7|N|8|@]|9|@]|A|$]]]|I|$]]

+Classification:
you are given some new data, you have to set new label for them.

For example, a company wants to classify their prospect customers. When a new customer comes, they have to determine if this is a customer who is going to buy their products or not.

+Clustering:
you're given a set of history transactions which recorded who bought what.

By using clustering techniques, you can tell the segmentation of your customers.

blocks|key|780490|text|分类-预测分类类标注-根据训练集和类标注属性中的值(类标注)对数据进行分类(构造模型)-使用模型对新数据进行分类|type|unstyled|depth|inlineStyleRanges|entityRanges|data|780491|集群:数据对象的集合--在同一集群中彼此相似--与其他集群中的对象不同|780492|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|F|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|G|8|@]|9|@]|A|$]]|$1|D|3|-4|5|6|7|H|8|@]|9|@]|A|$]]]|E|$]]

Classification
– Predicts categorical class labels
– Classifies data (constructs a model) based on a training set and the values (class labels) in a class label attribute 
– Uses the model in classifying new data

Cluster: a collection of data objects
– Similar to one another within the same cluster
– Dissimilar to the objects in other clusters

blocks|key|780580|text|我认为分类就是将数据集中的记录分类到预定义的类中，甚至在旅途中定义类。我认为它是任何有价值的数据挖掘的先决条件，我喜欢将其视为无监督学习，即一个人在挖掘数据时不知道他/她在寻找什么，分类是一个很好的起点|type|unstyled|depth|inlineStyleRanges|entityRanges|data|780581|在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临界水平。我相信这需要对统计学和数学有所了解。|780582|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|F|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|G|8|@]|9|@]|A|$]]|$1|D|3|-4|5|6|7|H|8|@]|9|@]|A|$]]]|E|$]]

I believe classification is classifying records in a data set into predefined classes or even defining classes on the go. I look at it as pre-requisite for any valuable data mining, I like to think of it at unsupervised learning i.e. one does not know what he/she is looking for while mining the data and classification serves as a good starting point

Clustering on the other end falls under supervised learning i.e. one know what parameters to look for, the correlation between them along with critical levels. I believe it requires some understanding of statistics and maths

blocks|key|780642|text|如果您正在尝试将大量的图纸归档到您的货架上(基于日期或文件的某些其他规范)，那么您就是在进行分类。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|780643|如果要从图纸集创建簇，这意味着在图纸之间有类似的东西。|780644|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|F|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|G|8|@]|9|@]|A|$]]|$1|D|3|-4|5|6|7|H|8|@]|9|@]|A|$]]]|E|$]]

If you are trying to file up a large number of sheets on to your shelf(based on date or some other specification of the file), you are CLASSIFYING.

If you were to create clusters from the set of sheets, it would mean that there is something similar among the sheets.

blocks|key|995042|text|聚类的目的是在数据中发现组。“集群”是一个直观的概念，没有严格的数学定义。一个集群的成员应该彼此相似，而与其他集群的成员不同。聚类算法在未标记的数据集Z上操作，并在其上产生分区。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|995043|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。一些类具有明确的含义，并且在最简单的情况下是相互排斥的。例如，在签名验证中，签名要么是真的，要么是伪造的。真正的类是两个类中的一个，不管我们可能无法通过观察特定的签名来正确地猜测。|995044|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|F|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|G|8|@]|9|@]|A|$]]|$1|D|3|-4|5|6|7|H|8|@]|9|@]|A|$]]]|E|$]]

Clustering aims at finding groups in data. “Cluster” is an intuitive concept and does
not have a mathematically rigorous definition. The members of one cluster should be
similar to one another and dissimilar to the members of other clusters. A clustering
algorithm operates on an unlabeled data set Z and produces a partition on it.

For Classes and Class Labels,
class contains similar objects, whereas objects from different classes
are dissimilar. Some classes have a clear-cut meaning, and in the simplest case
are mutually exclusive. For example, in signature verification, the signature is either
genuine or forged. The true class is one of the two, no matter that we might not be
able to guess correctly from the observation of a particular signature.

blocks|key|995418|text|我相信你们中的许多人都听说过机器学习。十几个人甚至可能知道它是什么。你们中的一些人可能也曾使用过机器学习算法。你们知道这是怎么回事了吗？没有多少人熟悉这项技术，这项技术在5年后将是绝对必要的。Siri是机器学习。亚马逊的Alexa是机器学习。广告和购物项目推荐系统都是机器学习。让我们用一个2岁男孩的简单类比来理解机器学习。为了好玩，我们就叫他Kylo+Ren吧|type|unstyled|depth|inlineStyleRanges|entityRanges|data|995419|​|995420|📷|atomic|offset|length|995421|995422|假设Kylo+Ren看到了一头大象。他的大脑会告诉他什么？(请记住，即使他是维德的继任者，他的思考能力也是最低的)。他的大脑会告诉他，他看到了一个灰色的大动物在移动。接着，他看到了一只猫，他的大脑告诉他，这是一只金色的小动物。最后，他看到了一把光剑，他的大脑告诉他，这是一个没有生命的物体，他可以玩！|995423|在这一点上，他的大脑知道军刀不同于大象和猫，因为军刀是可以玩耍的东西，不会自己移动。即使Kylo不知道移动是什么意思，他的大脑也能搞清楚这一点。这种简单的现象被称为聚类。|995424|995425|995426|995427|机器学习只不过是这个过程的数学版本。许多研究统计学的人意识到，他们可以让一些方程像大脑一样工作。大脑可以对相似的对象进行聚类，大脑可以从错误中学习，大脑可以学习识别事物。|995428|所有这些都可以用统计来表示，这个过程的基于计算机的模拟被称为机器学习。为什么我们需要基于计算机的模拟？因为计算机可以比人脑更快地完成繁重的数学计算。我很乐意进入机器学习的数学/统计部分，但你不想在没有明确一些概念的情况下跳到这一部分。|995429|让我们回到Kylo+Ren。比方说Kylo拿起军刀，开始玩它。他不小心撞上了冲锋队，冲锋队受伤了。他不知道发生了什么，继续玩。接下来，他撞上了一只猫，猫受伤了。这一次，Kylo确信他做了什么坏事，并试着有点小心。但考虑到他糟糕的剑术，他击中了大象，并确信自己有麻烦了。但之后他变得非常小心，只会故意打他的父亲，就像我们在《原力觉醒》中看到的那样！|995430|995431|995432|995433|从错误中学习的整个过程可以用等式来模拟，其中做错事的感觉是由错误或成本表示的。这个确定不能用军刀做什么的过程被称为分类。聚类和分类是机器学习的绝对基础。让我们来看看它们之间的区别。|995434|Kylo区分了动物和光剑，因为他的大脑认为光剑不能自己移动，因此是不同的。这一决定完全基于物体(数据)，没有提供任何外部帮助或建议。与此形成对比的是，Kylo通过首先观察打击物体可以做什么来区分小心使用光剑的重要性。这个决定并不完全基于军刀，而是基于它可以对不同的物体做什么。简而言之，这里有一些帮助。|995435|995436|995437|995438|由于学习中的这种差异，聚类被称为无监督学习方法，分类被称为监督学习方法。在机器学习世界中，这两种方法非常不同，并且通常由存在的数据类型决定。获取带标签的数据(或帮助我们学习的东西，如Kylo中的冲锋队、大象和猫)通常并不容易，当要区分的数据很大时，会变得非常复杂。另一方面，没有标签的学习也有它自己的缺点，比如不知道标签的标题是什么。如果Kylo在没有任何例子或帮助的情况下学会小心使用军刀，他不会知道它会做什么。他只会知道这是不应该做的。这是一个有点站不住脚的类比，但你明白了！|995439|我们才刚刚开始学习机器学习。分类本身可以是连续数字的分类，也可以是标签的分类。例如，如果凯洛必须对每个冲锋队士兵的高度进行分类，答案将会很多，因为高度可以是5.0、5.01、5.011等。但是像光剑类型(红色、blue.green)这样的简单分类的答案将非常有限。事实上，它们可以用简单的数字来表示。红色可以是0，蓝色可以是1，绿色可以是2。|995440|如果你知道基本的数学知识，你就会知道0,1,2和5.1,5.01,5.011是不同的，分别被称为离散和连续数字。离散数的分类称为Logistic回归，连续数的分类称为回归。Logistic回归也称为分类分类，所以当你在别处读到这个术语时，不要混淆。|995441|这是对机器学习的一个非常基本的介绍。我将在下一篇文章中详述统计方面的内容。如果我需要任何更正，请告诉我：)|995442|第二部分发布了here。|995443|995444|995445|entityMap|0|IMAGE|mutability|IMMUTABLE|imageUrl|https://ask.qcloudimg.com/http-save/yehe-900000/950b13ed78ae912d16db17a238cdfb9b.png|imageAlt|1|https://ask.qcloudimg.com/http-save/yehe-900000/08c7722635b6bbb7a1054106da34e328.png|2|https://ask.qcloudimg.com/http-save/yehe-900000/aad2620752adf3a9ee06f03063e95db8.png|3|https://ask.qcloudimg.com/http-save/yehe-900000/30425343db7c844a21b452fd85862b80.png|4|LINK|MUTABLE|url|https://medium.com/@darth_waiter/in-my-last-post-i-went-over-the-very-basics-of-machine-learning-c03f78bf3e84|5|https://ask.qcloudimg.com/http-save/yehe-900000/420edc8e6f287d336b70142a1b3c0d73.png^0|0|0|0|1|0|0|0|0|0|0|0|1|1|0|0|0|0|0|0|0|1|2|0|0|0|0|0|0|1|3|0|0|0|0|0|0|7|4|4|0|0|1|5|0|0^^$0|@$1|2|3|4|5|6|7|24|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|25|8|@]|9|@]|A|$]]|$1|D|3|E|5|F|7|26|8|@]|9|@$G|27|H|28|1|29]]|A|$]]|$1|I|3|C|5|6|7|2A|8|@]|9|@]|A|$]]|$1|J|3|K|5|6|7|2B|8|@]|9|@]|A|$]]|$1|L|3|M|5|6|7|2C|8|@]|9|@]|A|$]]|$1|N|3|C|5|6|7|2D|8|@]|9|@]|A|$]]|$1|O|3|E|5|F|7|2E|8|@]|9|@$G|2F|H|2G|1|2H]]|A|$]]|$1|P|3|C|5|6|7|2I|8|@]|9|@]|A|$]]|$1|Q|3|R|5|6|7|2J|8|@]|9|@]|A|$]]|$1|S|3|T|5|6|7|2K|8|@]|9|@]|A|$]]|$1|U|3|V|5|6|7|2L|8|@]|9|@]|A|$]]|$1|W|3|C|5|6|7|2M|8|@]|9|@]|A|$]]|$1|X|3|E|5|F|7|2N|8|@]|9|@$G|2O|H|2P|1|2Q]]|A|$]]|$1|Y|3|C|5|6|7|2R|8|@]|9|@]|A|$]]|$1|Z|3|10|5|6|7|2S|8|@]|9|@]|A|$]]|$1|11|3|12|5|6|7|2T|8|@]|9|@]|A|$]]|$1|13|3|C|5|6|7|2U|8|@]|9|@]|A|$]]|$1|14|3|E|5|F|7|2V|8|@]|9|@$G|2W|H|2X|1|2Y]]|A|$]]|$1|15|3|C|5|6|7|2Z|8|@]|9|@]|A|$]]|$1|16|3|17|5|6|7|30|8|@]|9|@]|A|$]]|$1|18|3|19|5|6|7|31|8|@]|9|@]|A|$]]|$1|1A|3|1B|5|6|7|32|8|@]|9|@]|A|$]]|$1|1C|3|1D|5|6|7|33|8|@]|9|@]|A|$]]|$1|1E|3|1F|5|6|7|34|8|@]|9|@$G|35|H|36|1|37]]|A|$]]|$1|1G|3|E|5|F|7|38|8|@]|9|@$G|39|H|3A|1|3B]]|A|$]]|$1|1H|3|C|5|6|7|3C|8|@]|9|@]|A|$]]|$1|1I|3|-4|5|6|7|3D|8|@]|9|@]|A|$]]]|1J|$1K|$5|1L|1M|1N|A|$1O|1P|1Q|-4]]|1R|$5|1L|1M|1N|A|$1O|1S|1Q|-4]]|1T|$5|1L|1M|1N|A|$1O|1U|1Q|-4]]|1V|$5|1L|1M|1N|A|$1O|1W|1Q|-4]]|1X|$5|1Y|1M|1Z|A|$20|21]]|22|$5|1L|1M|1N|A|$1O|23|1Q|-4]]]]

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

<a href="https://i.stack.imgur.com/bVaxZ.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/bVaxZ.png" alt="Source : blog.printkeg.com"></a>

Let’s assume Kylo Ren saw an elephant. What will his brain tell him ?(Remember he has minimum thinking capacity, even if he is the successor to Vader). His brain will tell him that he saw a big moving creature which was grey in color. He sees a cat next, and his brain tells him that it is a small moving creature which is golden in color. Finally, he sees a light saber next and his brain tells him that it is a non-living object which he can play with!

His brain at this point knows that saber is different from the elephant and the cat, because the saber is something to play with and doesn’t move on its own. His brain can figure this much out even if Kylo doesn’t know what movable means. This simple phenomenon is called Clustering .

<a href="https://i.stack.imgur.com/aTxuX.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/aTxuX.png" alt="Source : jeffcarpenterblog.tumblr.com"></a>

Machine learning is nothing but the mathematical version of this process.
A lot of people who study statistics realized that they can make some equations work in the same way as brain works. 
Brain can cluster similar objects, brain can learn from mistakes and brain can learn to identify things.

All of this can be represented with statistics, and the computer based simulation of this process is called Machine Learning. Why do we need the computer based simulation? because computers can do heavy math faster than human brains. 
I would love to go into the mathematical/statistical part of machine learning but you don’t wanna jump into that without clearing some concepts first.

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!!

<a href="https://i.stack.imgur.com/Wp7UV.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/Wp7UV.png" alt="Source : joyreactor.com"></a>

This entire process of learning from your mistake can be mimicked with equations, where the feeling of doing something wrong is represented by an error or cost. This process of identifying what not to do with a saber is called Classification . 
Clustering and Classification are the absolute basics of machine learning. Let’s look at the difference between them.

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.

<a href="https://i.stack.imgur.com/GaYED.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/GaYED.png" alt="Source: pintrest"></a>

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.

If you know basic math, you know that 0,1,2 and 5.1,5.01,5.011 are different and are called discrete and continuous numbers respectively. The classification of discrete numbers is called Logistic Regression , and classification of continuous numbers is called Regression. 
Logistic Regression is also known as categorical classification, so don’t be confused when you read this term elsewhere

This was a very basic introduction to Machine Learning. I will dwell into the statistical side in my next post. Please let me know if I need any corrections :)

Second part posted <a href="https://medium.com/@darth_waiter/in-my-last-post-i-went-over-the-very-basics-of-machine-learning-c03f78bf3e84" rel="nofollow noreferrer">here</a>.
<a href="https://i.stack.imgur.com/YBwvK.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/YBwvK.png" alt="enter image description here"></a>

blocks|key|781186|text|在数据挖掘中有两个定义：“有监督的”和“无监督的”。当有人告诉计算机，算法，代码，...这个东西就像一个苹果，那个东西就像一个橙子，这是监督学习，使用监督学习(就像数据集中每个样本的标签)来对数据进行分类，你就会得到分类。但另一方面，如果你让计算机找出什么是什么，并区分给定数据集的特征，实际上是学习无监督，以便对数据集进行分类，这将称为聚类。在这种情况下，提供给算法的数据没有标签，算法应该找出不同的类。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|781187|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|D|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|E|8|@]|9|@]|A|$]]]|C|$]]

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.

blocks|key|995537|text|聚类是一种对对象进行分组的方法，即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。它是一种常用的统计数据分析技术，用于机器学习和数据挖掘。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|995538|分类是一种基于训练数据集来识别、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，其中可以获得训练集和正确定义的观察值。|995539|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|F|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|G|8|@]|9|@]|A|$]]|$1|D|3|-4|5|6|7|H|8|@]|9|@]|A|$]]]|E|$]]

Clustering is a method of grouping objects in such a way that objects with similar features come together, and objects with dissimilar features go apart. It is a common technique for statistical data analysis used in machine learning and data mining.. 

Classification is a process of categorization where objects are recognized, differentiated and understood on the basis of the training set of data. Classification is a supervised learning technique where a training set and correctly defined observations are available.

blocks|key|995563|text|通过聚类，您可以使用所需的属性对数据进行分组，例如提取的聚类的数量、形状和其他属性。而在分类中，组的数量和形状是固定的。大多数聚类算法都将聚类的数量作为一个参数。但是，有一些方法可以找出合适的集群数量。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|995564|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|D|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|E|8|@]|9|@]|A|$]]]|C|$]]

By clustering, you can group data with your desired properties such as the number, the shape, and other properties of extracted clusters. While, in classification, the number and the shape of groups are fixed.
Most of the clustering algorithms give the number of clusters as a parameter. However, there are some approaches to find out the appropriate number of clusters.

blocks|key|781374|text|分类和聚类的主要区别是:分类是在分类标签的帮助下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。分类是与监督学习相结合的。与之相对，聚类也被称为无监督学习。在分类方法中提供训练样本，而在聚类训练数据的情况下不提供训练样本。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|781375|希望这能有所帮助！|781376|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|F|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|G|8|@]|9|@]|A|$]]|$1|D|3|-4|5|6|7|H|8|@]|9|@]|A|$]]]|E|$]]

The Key Differences Between Classification and Clustering are:
Classification is the process of classifying the data with the help of class labels. On the other hand, Clustering is similar to classification but there are no predefined class labels.
Classification is geared with supervised learning. As against, clustering is also known as unsupervised learning.
Training sample is provided in the classification method while in the case of clustering training data is not provided.

Hope this will help!

Can someone explain what the difference is between classification and clustering in data mining?

If you can, please give examples of both to understand the main idea.

问数据挖掘中分类和聚类的区别？
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