Which statement is NOT true about clustering methods?

Clustering methods are primarily focused on grouping data points based on similarity, not on reducing dimensionality. When discussing clustering, the main goal is to classify data into distinct groups (which is true for the first statement) and to help visualize the inherent structure of the data (as highlighted in the second statement). While clustering can create groupings that make it easier to understand data patterns, it does not specifically aim at reducing dimensionality or maintaining variance like other techniques such as Principal Component Analysis (PCA).

Furthermore, clustering methods are subject to interpretation, which can vary significantly based on the algorithm used, the choice of parameters, and the characteristics of the data itself. Thus, the statement indicating that clustering aims at reducing dimensionality while maintaining variance does not align with the fundamental purpose of clustering techniques.