Cluster analysis

Cluster Analysis

Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense) to each other than to those in other groups (clusters).

Pronunciation

/ˈklʌstər əˈnælɪsɪs/

Etymology

The term "cluster" comes from the Old English "clyster" meaning "a group or bunch" and the term "analysis" comes from the Greek "analusis" meaning "a breaking up, a loosening, releasing".

Definition

In data mining, cluster analysis is a descriptive task that seeks to identify homogeneous groups of objects based on the values of their attributes. Essentially, the aim is to segregate groups with similar traits and assign them into clusters.

Types of Cluster Analysis

There are several types of cluster analysis, including:

Hierarchical clustering: This method creates a hierarchical decomposition of the given set of data objects.

Partitioning clustering: This method constructs various partitions and then evaluates them by some criterion.

Density-based clustering: This method creates a partition of the data space into density-based clusters.

Grid-based clustering: This method quantizes the object space into a finite number of cells that form a grid structure.

Applications

Cluster analysis has a variety of applications, such as:

Market research: Cluster analysis is used to segment the market into distinct customer groups.

Image processing: Cluster analysis can be used to partition the input image into segments.

Bioinformatics: Cluster analysis is used to group genes with similar expression patterns.

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Cluster analysis