Difference between classification and clustering in data mining

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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

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Classification- A data-set can have different groups  classes  red  green and black  Classification will try to find rules that divides them in different classes        Custering- if a data-set is not having any class and you want to put them in some class grouping  you do clustering  The purple circles above    If classification rules are not good  you will have mis-classification in testing or ur rules are not correct enough    if clustering is not good  you will have lot of outliers ie  data points not able to fall in any cluster

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Machine Learning or AI is largely perceived by the task it Performs achieves   In my opinion  by thinking about Clustering and Classification in notion of task they achieve can really help to understand the difference between the two   Clustering is to Group things and Classification is to  kind of  label things   Let s assume you are in a party hall where all men are in Suits and women are in Gowns   Now  you ask your friend few questions   Q1  Heyy  can you help me group people   Possible answers that your friend can give are   1  He can group people based on Gender  Male or Female  2  He can group people based on their clothes  1 wearing suits other wearing gowns  3  He can group people based on color of their hairs  4  He can group people based on their age group  etc  etc  etc   Their are numerous ways your friend can complete this task   Of course  you can influence his decision making process by providing extra inputs like   Can you help me group these people based on gender  or age group  or hair color or dress etc    Q2   Before Q2  you need to do some pre-work   You have to teach or inform your friend so that he can take informed decision  So  let s say you said to your friend that    People with long hair are Women  People with short hair are Men    Q2   Now  you point out to a Person with long hair and ask your friend - Is it a Man or a Woman   The only answer that you can expect is  Woman   Of course  there can be men with long hairs and women with short hairs in the party  But  the answer is correct based on the learning you provided to your friend  You can further improve the process by teaching more to your friend on how to differentiate between the two   In above example   Q1 represents the task what Clustering achieves   In Clustering you provide the data people  to the algorithm your friend  and ask it to group the data    Now  it s up to algorithm to decide what s the best way to group is   Gender  Color or age group    Again you can definitely influence the decision made by the algorithm by providing extra inputs   Q2 represents the task Classification achieves   There  you give your algorithm your friend  some data People   called as Training data  and made him learn which data corresponds to which label Male or Female   Then you point your algorithm to certain data  called as Test data  and ask it to determine whether it is Male or Female  The better your teaching is  the better it s prediction   And the Pre-work in Q2 or Classification is nothing but just training your model so that it can learn how to differentiate  In Clustering or Q1 this pre-work is the part of grouping   Hope this helps someone   Thanks

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Classification  Predict results in a discrete output    map input variables into discrete categories    Popular use cases    Email classification   Spam or non-Spam Sanction loan to customer   Yes if he is capable of paying EMI for the sanctioned loan amount  No if he can t Cancer tumour cells identification   Is it critical or non-critical  Sentiment analysis of tweets   Is the tweet positive or negative or neutral Classification of news    Classify the news into one of predefined classes - Politics  Sports  Health etc   Clustering  is the task of grouping a set of objects in such a way that objects in the same group  called a cluster  are more similar  in some sense  to each other than to those in other groups  clusters       Popular use cases    Marketing   Discover customer segments for marketing purposes Biology   Classification among different species of plants and animals Libraries   Clustering different books on the basis of topics and information Insurance   Acknowledge the customers  their policies and identifying the frauds City Planning   Make groups of houses and to study their values based on their geographical locations and other factors  Earthquake studies   Identify  dangerous zones Recommendation system      References   geeksforgeeks  dataaspirant  3leafnodes

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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

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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

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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

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In general  in classification you have a set of predefined classes and want to know which class a new object belongs to   Clustering tries to group a set of objects and find whether there is some relationship between the objects   In the context of machine learning  classification is supervised learning and clustering is unsupervised learning   Also have a look at Classification and Clustering at Wikipedia

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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

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If you have asked this question to any data mining or machine learning persons they will use the term supervised learning and unsupervised learning to explain you the difference between clustering and classification  So let me first explain you about the key word supervised and unsupervised   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   This type of data you will get from the trained data  This type of learning is called as supervised learning  This type solving problem comes under Classification  So you already learn the things so you can do you job confidently   unsupervised   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   This time you don t  know any thing about that fruits  you are first time seeing these fruits so how  will you arrange the same type of fruits   What you will do first  is you take on the fruit and you will select any physical character of that particular fruit  suppose you taken color   Then you will arrange them based on the color  then the  groups will be some thing like this  RED COLOR GROUP  apples  amp  cherry fruits  GREEN COLOR GROUP  bananas  amp  grapes  so now you will take another physical character as size  so now the groups will be some thing like this  RED COLOR AND BIG SIZE  apple  RED COLOR AND SMALL SIZE  cherry fruits  GREEN COLOR AND BIG SIZE  bananas  GREEN COLOR AND SMALL SIZE  grapes  job done happy ending   here you didn t  learn any thing before  means no train data and no response variable  This type of learning is known unsupervised learning  clustering comes under unsupervised learning

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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    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       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      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     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 here

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Classification  Is the assignment of predefined classes to new observations  based on learning from examples   It is one of the key tasks in machine learning   Clustering  or Cluster Analysis   While popularly dismissed as  unsupervised classification  it is quite different   In contrast to what many machine learners will teach you  it is not about assigning  classes  to objects  but without having them predefined  This is the very limited view of people who did too much classification  a typical example of if you have a hammer  classifier   everything looks like a nail  classification problem  to you  But it is also why classification people do not get a hang of clustering   Instead  consider it as structure discovery  The task of clustering is to find structure  e g  groups  in your data that you did not know before  Clustering has been successful if you learned something new  It failed  if you only got the structure you already knew   Cluster analysis is a key task of data mining  and the ugly duckling in machine-learning  so don t listen to machine learners dismissing clustering     Unsupervised learning  is somewhat an Oxymoron  This has been iterated up and down the literature  but unsupervised learning is bllsht  It does not exist  but it is an oxymoron like  military intelligence    Either the algorithm learns from examples  then it is  supervised learning    or it does not learn  If all the clustering methods are  learning   then computing the minimum  maximum and average of a data set is  unsupervised learning   too  Then any computation  learned  its output  Thus the term  unsupervised learning  is totally meaningless  it means everything and nothing   Some  unsupervised learning  algorithms do  however  fall into the optimization category  For example k-means is a least-squares optimization  Such methods are all over statistics  so I don t think we need to label them  unsupervised learning   but instead should continue to call them  optimization problems   It s more precise  and more meaningful  There are plenty of clustering algorithms who do not involve optimization  and who do not fit into machine-learning paradigms well  So stop squeezing them in there under the umbrella  unsupervised learning    There is some  learning  associated with clustering  but it is not the program that learns  It is the user that is supposed to learn new things about his data set

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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 here

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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

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Please read the following information

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From book Mahout in Action  and I think it explains the difference very well   Classification algorithms are related to  but still quite different from  clustering algorithms such as the k-means algorithm  Classification algorithms are a form of supervised learning  as opposed to unsupervised learning  which happens with clustering algorithms  A supervised learning algorithm is one that   s given examples that contain the desired value of a target variable  Unsupervised algorithms aren   t given the desired answer  but instead must find something plausible on their own

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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

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One liner for Classification   Classifying data into pre-defined categories  One liner for Clustering   Grouping data into a set of categories  Key difference   Classification is taking data and putting it into pre-defined categories and in Clustering the set of categories  that you want to group the data into  is not known beforehand   Conclusion    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   I have written a long post on the same topic which you can find here    https   neelbhatt40 wordpress com 2017 11 21 classification-and-clustering-machine-learning-interview-questions-answers-part-i

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First of all  like many answers state here  classification is supervised learning and clustering is unsupervised  This means    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

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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

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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

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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

[machine-learning] Difference between classification and clustering in data mining?

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