Week 1 Lecture 3 - Unsupervised Learning

Summary

This module introduces unsupervised learning, contrasting it with supervised learning by highlighting the lack of labeled data. It focuses on clustering, where the goal is to identify groups of related data points and detect outliers. The script also covers association rule mining, which involves finding frequent patterns and conditional dependencies in data, with applications in market basket analysis and social network analysis. The importance of understanding data without predefined labels for various practical applications is emphasized.

Takeaways

• 📚 Unsupervised learning is about handling data without labels, unlike supervised learning which uses labeled training data.
• 🔍 The primary goal of unsupervised learning is clustering, which involves finding groups of data points that are closely related in the input space.
• 🤖 Bias in clustering is often assumed in the form of the shape of clusters, such as ellipsoids, which can influence how data is grouped.
• 👀 Outliers are data points that do not fit into any cluster and are considered anomalies in the dataset.
• 🛍️ Clustering can be applied to customer data to discover different types of customers, enabling targeted marketing strategies.
• 🖼️ Image clustering can help in segmenting different regions of an image, such as distinguishing clouds, sand, and sea in a beach scene.
• 📝 Association rule mining is a method to find frequent patterns and conditional dependencies in data, which can be used for making predictions or understanding relationships.
• 🛒 Market Basket analysis is a common application of association rule mining, where it identifies frequently bought items together to inform sales strategies.
• 🔢 The process of association rule mining typically involves two stages: finding frequent patterns and then deriving rules from these patterns.
• 📈 Time Series analysis and fault analysis are other applications where association rules can help identify sequences of events or causes of faults.
• 🔑 Terminology in association rule mining includes 'item set', which refers to a set or subset of items that are bought together in transactions.

Q & A

What is the primary difference between supervised and unsupervised learning?

Supervised learning involves training data with labels, whereas unsupervised learning deals with data without any labels, and the goal is to find patterns or groupings within the data.

What is the main objective of clustering in unsupervised learning?

The main objective of clustering is to find groups of coherent or cohesive data points in the input space, essentially discovering inherent structures in the data.

What is an example of bias in the context of clustering?

An example of bias in clustering is the assumption about the shape of clusters. The script mentions an assumption that clusters are ellipsoids, which influences how they are represented.

What are outliers in the context of clustering?

Outliers are data points that do not fall into any of the identified clusters, often considered as points that are far away from other points in the dataset and do not conform to the patterns.

Can you provide an example of how clustering can be applied in customer data?

Clustering can be applied to customer data to discover different classes of customers, allowing for targeted promotions and marketing strategies based on the similarities among customers.

How can clustering be used in image processing?

In image processing, clustering can be used to segment different regions of an image, such as distinguishing clouds, sand, and sea in a beach scene, which helps in making sense of the image content.

What is Association rule mining and how does it differ from other machine learning problems?

Association rule mining is a process of finding frequent patterns and conditional dependencies in data. It differs from other machine learning problems as it originated as a mining problem rather than a learning problem and focuses on pattern relationships rather than prediction.

What is the significance of Market Basket data in Association rule mining?

Market Basket data is significant in Association rule mining as it represents transactions where items are bought together. Analyzing this data can reveal frequent patterns of item purchases, which can be used to create association rules and understand customer buying behavior.

What are the two stages of the Association rule mining process?

The two stages of the Association rule mining process are: 1) Finding all frequent patterns or item sets in the data, and 2) Deriving association rules from these frequent patterns, identifying conditional dependencies among them.

How can the results of Association rule mining be applied in different settings?

The results of Association rule mining can be applied in various settings such as predicting co-occurrence of events, analyzing market basket data for retail recommendations, time series analysis for identifying triggers for certain events, and social network analysis for understanding interactions among entities.

What is the importance of understanding the terminology used in Association rule mining?

Understanding the terminology used in Association rule mining, such as 'item set' and 'frequent item sets', is important as it helps in accurately identifying and interpreting the patterns and rules derived from the data, which is crucial for making informed decisions.

Outlines

00:00

📚 Introduction to Unsupervised Learning

This paragraph introduces the concept of unsupervised learning, contrasting it with supervised learning where data comes labeled. The focus is on clustering, which aims to identify groups of similar data points without predefined labels. The speaker discusses the bias in assuming cluster shapes, typically ellipsoids, and the identification of outliers—data points that do not fit into any cluster. The paragraph also touches on the variety of clustering methods and their applications, such as customer segmentation for targeted marketing, image segmentation to identify distinct regions in a picture, and document clustering to discover topics in a collection. The speaker also introduces the concept of association rule mining, which involves finding frequent patterns and conditional dependencies in data, with a brief mention of its applications in market basket analysis and other areas.

05:04

🔍 Association Rule Mining and Its Applications

The second paragraph delves into the specifics of association rule mining, which is about discovering frequent patterns and the conditional relationships between them in datasets. The process is broken down into two stages: first, identifying frequent patterns or item sets, and second, deriving association rules from these patterns. The paragraph provides examples such as customer behavior in a store, where the frequent co-occurrence of certain customers can lead to insights about their shopping habits. It also mentions other applications, including time series analysis for event triggers, fault analysis by identifying sequences leading to faults, and social network analysis to find common interactions. The paragraph concludes with a brief explanation of terminology used in association rule mining, such as 'item set' and the concept of deriving rules from frequent item sets.

Keywords

💡Unsupervised Learning

Unsupervised Learning is a type of machine learning where the algorithm is trained on data without labeled responses. It's used to explore data and discover patterns or structures within it. In the video, it's the main theme, contrasting with supervised learning where data comes with labels. The script discusses how unsupervised learning can be applied to clustering and association rule mining without predefined classes or outcomes.

💡Clustering

Clustering is a method within unsupervised learning where the goal is to group a set of objects in such a way that objects in the same group are more similar to each other than to those in other groups. The script uses clustering as an example of finding 'coherent or cohesive data points' and identifies it as a way to discover natural groupings in data, such as grouping customers or image pixels.

💡Coherent Data Points

Coherent data points refer to elements within a dataset that are closely related or share similar characteristics, making them suitable to be grouped together in a cluster. The script mentions this concept while explaining the objective of clustering, where the data points form natural groups that are distinct from one another.

💡Ellipsoids

In the context of the script, ellipsoids represent a geometric assumption about the shape of clusters. The speaker mentions having a bias towards ellipsoid shapes when identifying clusters, implying that the algorithm assumes clusters to be ellipsoidal in form, which influences how data points are grouped.

💡Outliers

Outliers are data points that do not fit well into any cluster and are significantly different from other observations. The script discusses outliers in the context of clustering, noting that not all data points need to fall into clusters, and some may be distant from the rest, thus considered outliers.

💡Association Rule Mining

Association Rule Mining is a technique used to discover interesting associations or correlations between variables in large databases. The script introduces this concept as a form of 'mining' that involves finding frequent patterns and conditional dependencies among these patterns, such as in market basket analysis.

💡Frequent Patterns

Frequent patterns are events or items that occur together in a dataset more often than would be expected by chance. The script discusses the importance of identifying these patterns in association rule mining, where they form the basis for deriving rules like 'if A happens, then B is likely to happen'.

💡Conditional Dependencies

Conditional dependencies refer to the relationships between different items or events where the presence of one item or event implies the presence of another. The script uses the example of customers A and B visiting a shop together to illustrate how these dependencies can be used to make predictions or inferences about customer behavior.

💡Market Basket Analysis

Market Basket Analysis is a specific application of association rule mining that involves analyzing customer purchases to find associations between different products. The script mentions this as a way to understand what items are frequently bought together, which can help in creating targeted promotions or understanding customer behavior.

💡Data Mining

Data Mining is the process of extracting useful information from large sets of data. The script briefly touches on data mining, noting that many tasks labeled as data mining are essentially machine learning problems. It also distinguishes 'mining' from 'learning' in the context of association rules.

💡Item Sets

In the context of association rule mining, item sets refer to groups of items that are purchased together. The script mentions that finding 'frequent item sets' is the first step in association rule mining, which then allows for the derivation of rules based on these sets.

Highlights

Introduction to unsupervised learning, contrasting with supervised learning which uses labeled data.

Unsupervised learning involves handling data without any labels attached.

Clustering is a key problem in unsupervised learning, aiming to find groups of coherent data points.

Example of identifying potential clusters in a dataset with different shapes.

Assumption of ellipsoid shapes for clusters based on bias in unsupervised learning.

Outliers are data points that do not fall into any clusters, identified as a result of the ellipsoid assumption.

Clustering aims to find cohesive groups and outliers that do not conform to input patterns.

Numerous methods exist for accomplishing clustering, which will be explored in the course.

Applications of clustering include discovering classes of customers from customer data.

Clustering can enable targeted promotions based on customer groupings without predefined labels.

Image clustering can segment different regions in an image, aiding in understanding the scene.

Word usage clustering can discover synonyms, while document clustering can identify similar topics.

Association rule mining involves finding frequent patterns and conditional dependencies in data.

Association rules can predict co-occurrences, like customers visiting a shop together.

The process of association rule mining includes two stages: finding frequent patterns and deriving associations.

Market Basket data is a popular example for association rule mining, analyzing items bought together.

Frequent item sets are the basis for deriving association rules in transaction data.

Association rule mining has applications in predicting event co-occurrences, market basket analysis, and time series analysis.

Terminology in association rule mining includes item sets and the implications between them.

The module concludes with an overview of unsupervised learning applications and methods.

Transcripts

Hello and welcome to this module on introduction  to unsupervised learning, right. So in supervised  

learning we looked at how you will handle  training data that had labels on it.

So this is this particular place this  is a classification data set where  

red denotes one class and blue  denotes the other class right.

And in unsupervised learning right so you  basically have a lot of data that is given  

to you but they do not have any labels  attached to them right. So we look at  

first at the problem of clustering where your  goal is to find groups of coherent or cohesive  

data points in this input space right so  here is an example of possible clusters.

So those set of data points could form a cluster  right and again now those set of data points could  

form a cluster and again those and those. So there  are like four clusters that we have identified in  

this in this setup. So one thing to note here  is that even in something like clustering so  

I need to have some form of a bias right so in  this case the bias that I am having is in the  

shape of the cluster so I am assuming that the  clusters are all ellipsoids right and therefore  

you know I have been drawing a specific  shape curves for representing the clusters.

And also note that not all data points need to  fall into clusters and there are a couple of  

points there that do not fall into any of the  clusters this is primarily a artifact of me  

assuming that they are ellipsoids but still  there are points in the center is actually  

faraway from all the other points in the in the  data set to be considered as what are known as  

outliers. So when you do clustering so there are  two things so one is you are interested in finding  

cohesive groups of points and the second is you  are also interested in finding data points that  

do not conform to the patterns in the input  and these are known as outliers all right.

And there are many many different ways in which  you can accomplish clustering and we will look  

at a few in the course. And the applications are  numerous right so here are a few representative  

ones. So one thing is to look at customer data  right and try to discover classes of customers.  

So earlier we looked at in the supervised learning  case we looked at is that a customer will buy a  

computer or will not buy a computer. As opposed to  that we could just take all the customer data that  

you have and try to just group them into different  kinds of customers who come to your shop and then  

you could do some kind of targeted promotions  and different classes of customers right.

And this need not necessarily come with labels  you know I am not going to tell you that okay  

this customer is class 1 that customer  is class 2 you are just going to find  

out which of the customers are more similar  with each other all right. And as the second  

application which we have illustrated here is  that I could do clustering on image pixels so  

that you could discover different regions  in the image and then you could do some  

segmentation based on that different region  so for example here it have a picture of a  

beach scene and then you are able to figure  out the clouds and the sand and the sea and  

the tree from the image. So that allows you  to make more sense out of the image right.

Or you could do clustering on word usages right  and you could discover synonyms and you could  

also do clustering on documents right and  depending on which kind of documents are  

similar to each other; if I give you  a collection of say 100,000 documents  

I might be able to figure out what are  the different topics that are discussed  

in this collection of documents and many  many ways in which you can use clustering.

Rule mining: I should give you a side about  the usage of the word mining here so many of  

you might have heard of the term data mining  and more often than not the purported data  

mining tasks are essentially machine learning  problems right so it could be classification  

regression and so on so forth. And the first  problem that was essentially introduced as a  

mining problem and not as a learning problem  was the one of mining frequent patterns and  

associations. And that is one of the reasons  I call this Association rule mining as opposed  

to Association rule learning just to keep  the historic connection intact right. So  

in Association rule mining we are interested in  finding frequent patterns that occur in the input  

data and then we are looking at conditional  dependencies among these patterns right.

So for example if A and B occur together  often right then I could say something like  

if A happens then B will happen let us suppose  that so you have customers that are coming to  

your shop and whenever customer A visits your  shop custom B also tags along with him right,  

so the next time you find customary  A somewhere in the shop so you can  

know that customer B is already  there in the shop along with A.

Or with very high confidence you could  say that B is also in the shop at some  

somewhere else may be not with A. But  somewhere else in the shop all right,  

so these are the kinds of rules that we  are looking at Association rules which are  

conditional dependencies – if A has come then  B is also there right and so the Association  

rule mining process usually goes in two stages so  the first thing is we find all frequent patterns.

So A happens often so A is a customer that comes  to my store often. And then I find that A and  

B are pairs of customers that come to my store  often. So if I once I have that right A comes to  

my store often and A and B comes to my store often  then I can derive associations from these kinds of  

frequent patterns. And also you could do this in  the variety of different settings you could find  

sequences in time series data right and where  you could look at triggers for certain events.

Or you could look at fault analysis right by  looking at a sequence of events that happened  

and you can figure out which event occurs more  often with a fault right or you could look at  

transactions data which is the most popular  example given here is what is called Market  

Basket data. So you go to a shop and you  buy a bunch of things together and you put  

them in your basket; so what is there in your  basket right so this forms the transaction so  

you buy say eggs, milk and bread and so  all of this go together in your basket.

And then you can find out what are the  frequently occurring patterns in this  

purchase data and then you can make rules out of  those or you could look at finding patterns and  

graphs that is typically used in social network  analysis so which kind of interactions among  

entities happen often right so that is another  question that is what we looking at right.

So the most popular thing here is mining  transactions so the most popular application  

here is mining transactions. And as I mentioned  earlier transaction is a collection of items  

that are bought together right and so here  is a little bit of terminology. A set or a  

subset of items is often called an item set  in the Association rule mining community  

and so the first step that you have to  do is find frequent item sets right.

And you can conclude that item set A, if  it is frequent implies item set B if both  

A and A union B or frequent item sets  right so A and B are subset so A union  

B is another subset so if both A and  A union B are frequent item sets then  

you can say that item set A implies item  set B right. Like I mentioned earlier so  

there are many applications here so you could  think of predicting co-occurrence of events.

And Market Basket analysis and Time Series  analysis like I mentioned earlier you could  

think of trigger events or causes of  Faults and so on so forth right so this  

brings us to the end of this module  introducing unsupervised learning.