Cluster Analysis Unsupervised Machine Learning Course Bundle

Cluster Analysis Unsupervised Machine Learning Course Bundle | Udemy
English | Size: 3.17 GB
Genre: eLearning

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

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 analysis

Cluster 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 characteristics

Cluster 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 criteria

In data mining, cluster analysis is used to gain in-depth understanding about the characteristics of data in each cluster.

Clustering Methods

Clustering methods can be divided into the following categories

Partitioning method

Hierarchical Method

Density based method

Grid Based Method

Model Based Method

Constraint Based Method

Advantages of Cluster Analysis

Given below are the advantages of cluster analysis

Cluster analysis gives a quick overview of data

It can be used if there are many groups in data

Cluster analysis can be used when there are unusual similarity measures to be done

Cluster analysis can be added on ordination plots and it is good for the nearest neighbours

Approaches to cluster analysis

There are a number of different approaches used to carry out cluster analysis which are divided into two

Hierarchical Method – Agglomerative Methods and Divisive Methods

Non Hierarchical Method also known as K-means Clustering methods

Cluster Analysis Course Objectives

At the end of this course you will be able to know

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

Who this course is for:
Students, Research professionals, Data Analysts, Data Miners And anyone who is interested in learning about cluster analysis

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