Cluster Analysis Unsupervised Machine Learning Course Bundle

Data science techniques for pattern recognition, data mining, k-means clustering, and hierarchical clustering etc.

What you'll learn

How to use cluster analysis in data mining

About the various types of clusters

About the Marketing applications of cluster analysis

Implications of wide variety of clustering techniques

Use clustering in statistical analysis

Requirements

Basic knowledge of statistics is required. Some familiarity with data analysis will be considered as an added advantage though it is not a necessity.

Description

Cluster Analysis is a statistical tool which is used to classify objects into groups called clusters, where the objects belonging to one cluster are more similar to the other objects in that same cluster and the objects of other clusters are completely different. In simple words cluster analysis divides data into clusters that are meaningful and useful. Clustering is used mainly for two purposes – clustering for understanding and clustering for utility.Application of cluster analysisCluster analysis is used in many fields like machine learning, market research, pattern recognition, data analysis, information retrieval, image processing and data compression.Cluster analysis can help the marketers to find out distinct groups of their customer base.Cluster analysis is used in the field of biology to find out plant and animal taxonomies and categorize genes with similar characteristicsCluster analysis is used in an earth observation database to group the houses in a city according to the house type, value and location.Clustering can also be used to segment the documents on the web based on a specific criteriaIn data mining, cluster analysis is used to gain in-depth understanding about the characteristics of data in each cluster.Clustering MethodsClustering methods can be divided into the following categoriesPartitioning methodHierarchical MethodDensity based methodGrid Based MethodModel Based MethodConstraint Based MethodAdvantages of Cluster AnalysisGiven below are the advantages of cluster analysisCluster analysis gives a quick overview of dataIt can be used if there are many groups in dataCluster analysis can be used when there are unusual similarity measures to be doneCluster analysis can be added on ordination plots and it is good for the nearest neighboursApproaches to cluster analysisThere are a number of different approaches used to carry out cluster analysis which are divided into twoHierarchical Method – Agglomerative Methods and Divisive MethodsNon Hierarchical Method also known as K-means Clustering methodsCluster Analysis Course ObjectivesAt the end of this course you will be able to knowHow to use cluster analysis in data miningAbout the various types of clustersAbout the Marketing applications of cluster analysisImplications of wide variety of clustering techniquesUse clustering in statistical analysis

Overview

Section 1: Cluster Analysis and Unsupervised Machine Learning with MS Excel

Lecture 1 Introduction to Project

Lecture 2 Data Introduction

Lecture 3 Data Format and Selection

Lecture 4 Clustering Phase Part 1

Lecture 5 Clustering Phase Part 2

Lecture 6 Clustering Phase Part 3

Lecture 7 Clustering Phase Part 4

Lecture 8 Clustering Phase Part 5

Lecture 9 Clustering Phase Part 6

Lecture 10 Clustering Phase Part 7

Lecture 11 Clustering Phase Part 8

Lecture 12 Scatter Plot

Lecture 13 Cluster Analysis Final Phasing

Lecture 14 Scatter Plot

Lecture 15 Conclusion

Section 2: Cluster Analysis and Unsupervised Machine Learning

Lecture 16 Introduction of Project

Lecture 17 Import Libraries

Lecture 18 Data Preprocessing

Lecture 19 Pie chart

Lecture 20 Histogram

Lecture 21 Violin plot

Lecture 22 Distribution Plot Analysis

Lecture 23 Pair plot and Female Data Analysis

Lecture 24 Male Data Analysis

Lecture 25 Male Data Analysis Continue

Lecture 26 Correlation Analysis

Lecture 27 Modelling

Lecture 28 Cluster Prediction

Lecture 29 Shopping Analysis

Section 3: Cluster Analysis and Unsupervised Machine Learning

Lecture 30 Introduction to Project

Lecture 31 Clustering Overview

Lecture 32 Data Explanation

Lecture 33 Clustering Algorithm

Lecture 34 Clustering using scaled Variables

Section 4: Cluster Analysis and Unsupervised Machine Learning - Basic Concepts

Lecture 35 Meaning of Cluster Analysis

Lecture 36 Understanding Cluster Analysis through example

Lecture 37 Example on Cluster Analysis (continues)

Lecture 38 Hierarchical method of Clustering

Lecture 39 Single link clustering

Lecture 40 1-Linkage method,Wards method,k means clustering

Lecture 41 K means and Example of K means, difference between heirarchic

Lecture 42 Example of K means no. of cluster, Statistical tests, Dendogram, scree plot

Lecture 43 Two step cluster analysis.,Evaluation

Lecture 44 Example for Listwise and Pairwise deletion of missing values , SPSS windows of o

Lecture 45 K means cluster theory, spss windows for k means, listwise and pairwise deletion

Lecture 46 Two step cluster analysis

Students, Research professionals, Data Analysts, Data Miners And anyone who is interested in learning about cluster analysis