Classify a set of observations into two or more mutually exclusive groups with similar characteristics.
> **Note:** This is depreciated. How can we predict groups of credit cardholders’ behaviors in order to reduce the charge-off risk of credit card issuers? How can we define groups of personality traits of employees in order to improve their performance at work? How can doctors classify patients into groups based on the characteristics of their diseases? In principle, all of these questions can be answered through cluster analysis. Cluster Model API is an example built with Microsoft Azure Machine Learning that classifies a set of observations into two or more mutually exclusive unknown groups based on combinations of variables. The purpose of cluster analysis is to discover a system of organizing observations, usually people or their characteristics, into groups, where members of the groups share properties in common. This web service uses the K-Means methodology, a commonly used clustering technique, to cluster arbitrary data into groups. This web service takes the data and the number of clusters k as input, and produces predictions of which of the k groups to which each observations belongs. There are multiple ways of consuming the service in an automated fashion (an example app is [here](http://microsoftazuremachinelearning.azurewebsites.net/ClusterModel.aspx)). *While this web service could be consumed by users - potentially through a mobile app, website, or even on a local computer for example, the purpose of the web service is also to serve as an example of how Azure ML can be used to create web services on top of R code. With just a few lines of R code and clicks of a button within the Azure ML Studio, an experiment can be created with R code and published as a web service. The web service can then be published to the Azure Marketplace and consumed by users and devices across the world with no infrastructure set-up by the author of the web service.*