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Cluster Analysis

Definition of Cluster Analysis

Cluster analysis is a statistical technique used to group objects that are similar to each other into respective categories known as clusters. It is a method of unsupervised learning, which means it seeks to identify inherent structures within a data set without using predefined classifications. Clusters are formed such that items in the same cluster are more closely related to one another than to those in other clusters.

Origin of Cluster Analysis

The origins of cluster analysis can be traced back to early anthropological and genetic studies where it was used to categorize plants and animals based on characteristics. Its mathematical foundations were laid in the early 20th century, but it wasn't until the advent of computers that cluster analysis became a widely used tool in many disciplines due to the intensive computations required to process large datasets.

Practical Application of Cluster Analysis

Cluster analysis has numerous practical applications across various fields. In marketing, it is used to segment customers into groups based on purchasing patterns, which can then inform targeted advertising strategies. In biology, it helps in classifying plants or genes with similar traits. In healthcare, cluster analysis can identify patient groups with similar symptoms for more precise medical interventions.

Benefits of Cluster Analysis

The benefits of cluster analysis are significant. It enables organizations to discover patterns and relationships in data that are not readily apparent. This can lead to more informed decision-making and strategy development. Cluster analysis also contributes to efficiency by automating the grouping process, allowing for quick analysis of large datasets that would be impractical to sort through manually. Additionally, it facilitates personalized services and product development by understanding customer segments better.


Cluster analysis is an unsupervised learning technique that groups data based on similarity without pre-labeled classes, while classification is a supervised learning technique that assigns data to predefined classes.

Cluster analysis is not predictive; it is descriptive. It identifies grouping based on existing data. However, the insights gained can be used to make predictions through other analytical methods.

While cluster analysis can process large datasets efficiently, it is also valuable for smaller datasets where the intrinsic structure or grouping is not known. It can provide insights regardless of the size of the dataset.


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