Cluster Analysis Button

Click the button to display the Clustering Method Startup Panel. Cluster Analysis encompasses a number of different classification algorithms that can be used to develop taxonomies (typically as part of exploratory data analysis).

The Cluster Analysis module includes a comprehensive implementation of clustering methods (k-means, hierarchical clustering, 2-way joining). STATISTICA can process data from either raw data files or matrices of distance measures (e.g., correlation matrices), and can cluster cases, variables, or both based on a wide variety of distance measures (including Euclidean, squared Euclidean, City-block (Manhattan), Chebychev, Power distances, Percent disagreement, and 1-r) and amalgamation/linkage rules (including single, complete, weighted and unweighted group average or centroid, Ward's method, and others). Matrices of distances can be saved for further analysis with other modules of the STATISTICA system. In k-means clustering, you have full control over the initial cluster centers.

Alternative methods for detecting clusters (structure) in observations and/or variables are available in Factor Analysis, Principal Components and Classification Analysis, Correspondence Analysis, and STATISTICA Automated Neural Networks.