Cluster Analysis is a statistical technique of classification, where small cases, operational data, and objects (like individuals, non-living things, locations, events, etc.) are sub-divided into small groups or clusters. The divisions are made in such a manner, that couple of items in one cluster are quite similar (but not exactly identical) to each other and are also absolutely different from the items grouped in other clusters. Cluster analysis is more of a discovery tool which is used for exploratory data analysis, and that reveals the different associations, structural patterns, relationships and also the structural masses of data. In simple words, cluster analysis acts as a tool of discovery for solving the various issues associated with classification.

What are the different methods of cluster analysis?

Cluster analysis is conducted via two methods, namely;

• Hierarchical Cluster Analysis methods
• Non-Hierarchical Cluster analysis methods

Hierarchical cluster analysis methods

The Hierarchical cluster analysis methods are further subdivided into the agglomerative methods and the divisive methods. In the agglomerative methods, all the objects start being in grouped in different clusters, till the similar objects are grouped. The process is constantly repeated until all the objects are grouped in a proper cluster. Finally, all the options are well analyzed to choose the optimum number of clusters.

The divisive method is a reversal of the agglomerative method. Here, all the objects get started in a similar cluster and eventually get split into the dissimilar ones. The Hierarchical cluster analysis methods are usually implemented when small sets of data are involved in the process.

Non-hierarchical cluster analysis methods

The non-hierarchical methods for cluster analysis are used when huge data sets are involved in the process. These methods provide better flexibility of moving a subject from one specific cluster to another. The non-hierarchical methods are also quite easy to implement.

What are the benefits and practical application of cluster analysis?

Cluster analysis also comes with a series of benefits. The main benefit of this procedure is the fact that it allows you to create small groups of similar (not identical) data. This helps to identify the patterns between several elements of data. It also reveals the associations between the various data objects thereby helping us to outline a structure which may not be visible apparently, but gives a better and a greater sense of meaning to the data after its discovery. Soon, when a clear structural body emerges, it allows better and effective decision-making.

Cluster analysis also has a very significant role in the field of marketing. In this field, it is used for the segmentation and positioning of the market. This analysis is also used for testing markets for the developments of new products. Again, in the fields of social media and networking, cluster analysis is used for identifying similar communities within apparently bigger groups. This technique has also been widely used in the field of biology and medical sciences for the clustering of genetics, sequencing of gene families and building small groups of genes. Cluster analysis can also be used to cluster, individuals, species and even genes at higher levels.