Which statement about cluster analysis is false?

The statement indicating that cluster analysis is supervised classification with k-means is indeed false. Cluster analysis, including the k-means method, is an unsupervised learning technique, meaning that it does not rely on a target variable or prior knowledge of class labels. Instead, it groups data points based solely on their characteristics and similarities. The goal of cluster analysis is to identify natural groupings within the data without the need for labeled outcomes, which distinguishes it from supervised classification techniques that do require such labels for training models.

The other statements accurately describe aspects of cluster analysis. For example, the fact that there is no target variable for cluster analysis is a fundamental characteristic of this method. Additionally, the Segment Profile tool in SAS Enterprise Miner is designed to help users understand the attributes of clusters formed during analysis. Moreover, while it is true that SAS Enterprise Miner employs k-means as a method for performing cluster analysis, this does not involve any form of supervision regarding class labels or predictions. These distinctions clarify why the statement about cluster analysis being a form of supervised classification is misleading.