In a general sense, a cluster is a term used for a group of similar objects or people existing or occurring closely together. Usually, within a larger population of similar and dissimilar data objects, clustering focuses on grouping these objects into smaller groups.
Today, the concept of clustering is widely applied in business analytics. Businesses are tasked with the challenge of identifying patterns in the vast, unstructured data they collect and organizing them into sensible structures. In other situations, they also have to extract smaller homogeneous groups from a larger heterogeneous population.
With cluster analysis, an exploratory data analysis tool, businesses can explore vast, unstructured volumes of data, and sort data objects into groups according to the degree of association between them.
Types of Clustering
There are many algorithms used for clustering, and each of these algorithms often produce different set of clusters as results. In a broader sense, there are two types of clustering methods: non-hierarchical and hierarchical methods.
Non-hierarchical methods, such as K-means, produce M clusters from a dataset of N objects. On the other hand, hierarchical clustering algorithms produce a set of nested clusters where each pair of clusters is successively nested in a bigger cluster until just a cluster is left.
Several factors determine what particular clustering method to choose. Such factors include the objective of the cluster analysis, the size of the dataset, the desired output, and the software and hardware facilities available.
Business Applications of Cluster Analysis
• Market Segmentation: Clustering techniques help companies divide large markets into homogeneous groups of consumers with similar interests, attributes or features. This helps businesses to strategically position themselves to tailor products and services for these distinct segments.
• Analyzing and Understanding Buyer Behaviors: With cluster analysis, organizations can identify homogeneous groups of buyers. For example, the purchasing patterns of each group can be analyzed separately on features like favorite stores, preferred size, brand loyalty, desired price, frequency of purchase, etc.
• Anomaly Detection: Cluster analysis can also help businesses identify unusual occurrences such as fraudulent transactions. Results from cluster analysis yield cleaned clusters which eventually show good transactions. When the shape and size of these desired or “normal” clusters are known, data objects or transactions outside of this cluster can be flagged and duly investigated.
• Identification of New Product Opportunities: When data objects such as brands and products are compared in a cluster analysis, competitive items within the market can be identified. Brands or products in the same cluster means they share common attributes and are therefore seen as fiercer rivals when compared to brands in other clusters. With this result, an organization can carefully analyze its current products and services relative to its close competitors in the same market to identify potential new product offerings.
• Data Reduction: At the beginning of a cluster analysis, researchers often have to explore a vast amount of unstructured data that initially appear meaningless. However, at the end of the analysis, the larger population is reduced into homogenous, meaningful groups and the irrelevant data objects can be ignored and filtered out.
In summary, clustering is a powerful, undirected data mining technique used in business analytics for identifying hidden patterns and structures in a vast volume of data without formulating a specific hypothesis. Various clustering methods exists, so the analyst must be able to choose of the best clustering algorithm just suitable to meet the needs of the business.
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