Cluster Analysis

What is Cluster Analysis?

Finding groups of objects such that the objects in a group will be similar (or related) to one another and different from (or unrelated to) the objects in other groups.

• Based on information found in the data that describes the objects and their relationships.
• Also known as unsupervised classification.

Many applications

• Understanding: group related documents for browsing or to find genes and proteins that have similar functionality.
• Summarization: Reduce the size of large data sets.

Web Documents are divided into groups based on a similarity metric.

• Most common similarity metric is the dot product between two document vectors.

What is not Cluster Analysis?

Supervised classification.

• Have class label information.

Simple segmentation.

• Dividing students into different registration groups alphabetically, by last name.

Results of a query.

• Groupings are a result of an external specification.

Graph partitioning

• Some mutual relevance and synergy, but areas are not identical.

Types of Clusterings

A clustering is a set of clusters.

One important distinction is between hierarchical and partitional sets of clusters.

Partitional Clustering

• A division data objects into non-overlapping subsets (clusters) such that each data object is in exactly one subset.

Hierarchical clustering

• A set of nested clusters organized as a hierarchical tree.

DIAW Serigne
Data Engineer, Data Scientist at Business and Decision
paper : https://www.ieee.org.ar/downloads/Srivastava-tut-pres.pdf