Clustering Analysis in Data Science

Introduction of Clustering

A Cluster is a group of servers and other resources that act like a single system and enable high availability and, in some cases, load balancing and parallel processing.  

What is it?

• Divides data into groups (clusters or segments or partitions)
• Unsupervised learning

Why do this?

• For better understanding. Examples:
• Marketing/Sales. know your customer.
• Communicating information
• Biology,Climate, Medicine, etc.

For some Utility.Mainly as precursor to further Data Analysis.

Where is it Used?

• Amazon    (Recommendation System)
• Netflix      (Recommendation Movies)
• Flickr        (Recommendation Photo's)

How business use Clustering

• Retailer Store
• Banking
• Insurance Companies

Types of Clustering and Cluster

Types of Clustering

• Exclusive Clustering
• overlapping Clustering
• Hierarchical Clustering

Exclusive Clustering :-

• Hard Clustering
• Data point/ Item belong exclusively to one cluster
• For example : K-means Clustering

  

Overlapping Clustering :-

• Soft Cluster
• Data point/Item belong to multiple cluster
• For example : Fuzzy/ C-Means Cluster

Hierarchical Clustering :-

Types of Cluster

• Well Separated
• Prototype Based
• Graph Based
• Density Based