K-Means Clustering Model Using Python | Data Science Algorithms | Ntirawen | Data Science

Agenda :

Introduction to Machine Learning

Cluster analysis

Types of clustering

Introduction to K-means clustering

How K-means clustering work ?

Demo in Python : Credit Card Company

 

 Introduction to Machine Learning :

Machine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed.

 

ML use Case - Google self driving car

Google self driving car is a smart , driverless car.

It collects data from environment through sensors.

Takes decisions like when to speed up, when to speed down , when to overtake and when to turn.

 

Types of Machine Learning 

Supervised Learning :

Feed the classifier with training data set and predefined labels.

It will learn to categorize particular data under a specific label.

 Unsupervised Learning :

An image of fruits is first fed into the system.

The system identifies different fruits using features like color, size and it categorizes them.

When a new fruit is shown, it analyses its features and puts it into the category having similar featured items.

What is Clustering ?

Clustering means grouping of objects based on the information found in the data describing the objects or their relationship.

The goal is that objects in one group should be similar to each but different from objects in another group.

It deals with finding a structure in a collection of unlabeled data.

Some Examples of clustering methods are :

• K-means Clustering
• Fuzzy/ C-means Clustering
• Hierarchical Clustering
  

 Clustering Use Case 

Marketing : Discovering distinct groups in customer databases, such as customers who make lot of long-distance calls.

Insurance : Identifying groups of crop insurance policy holders with a high average claim rate. farmers crash crops, when it is "profitable".

Land use : Identification of areas of similar land use in a GIS database.

seismic studies : Identifying probable areas for oil/gas exploration based on seismic data.

 

Types of Clustering 

Exclusive Clustering 

An item belongs exclusively to one cluster, not several .

K-means does this sort of exclusive clustering.

 

Overlapping Clustering

An item can belong to multiple clusters.

Its degree of association with each cluster is known

Fuzzy/ C-means does this sort of exclusive clustering.

 

Hierarchical Clustering

When two cluster have a parent-child relationship or a tree-like structure then it is Hierarchical clustering.

 

K-means Clustering : K-means clustering is one of the simplest algorithms which use unsupervised learning method to solve known clustering issues.

Divides entire dataset into k clusters..

K-means clustering require following two inputs. 

1. K = number of clusters

2. Training set(m) = { x1, x2, x3, ......., xm}

Example - Google News

• Various news URLs related to Trump and Modi are grouped under one section.
• K-means clustering automatically clusters new stories about the same topic into pre-defined clusters.