Unsupervised learning method is used because there is no target field in this case. Unsupervised learning method is used to uncover meaningful patterns in the data. Clustering is often called an unsupervised learning task as no class values denoting an a priori grouping of the data instances are given. Analysis of Case study Contd..
Cluster Analysis: Basic Concepts and Algorithms - Case Study Example
A Tutorial on People Analytics Using R – Clustering | AIHR Analytics
In the context of customer segmentation , cluster analysis is the use of a mathematical model to discover groups of similar customers based on finding the smallest variations among customers within each group. The goal of cluster analysis in marketing is to accurately segment customers in order to achieve more effective customer marketing via personalization. A common cluster analysis method is a mathematical algorithm known as k-means cluster analysis , sometimes referred to as scientific segmentation. The clusters that result assist in better customer modeling and predictive analytics , and are also are used to target customers with offers and incentives personalized to their wants, needs and preferences. The process is not based on any predetermined thresholds or rules. Rather, the data itself reveals the customer prototypes that inherently exist within the population of customers.
What is cluster analysis and when should you use it?
Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. DOI: It's the means to cluster customer reasonably and effectively in improving economic efficiency of enterprises.
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