Statistics - Multivariate Exploratory Techniques - Cluster Analysis

Ribbon bar. Select the Statistics tab. In the Advanced/Multivariate group, click Mult/Exploratory and from the menu, select Cluster to display the Clustering Method dialog box.

Classic menus. From the Statistics - Multivariate Exploratory Techniques submenu, select Cluster Analysis to display the Clustering Method dialog box.

Cluster analysis encompasses a number of different classification algorithms that can be used to develop taxonomies (typically as part of exploratory data analysis).

This 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). Additional advanced methods for Em, k-Means, and Tree clustering are also available via the Cluster Analysis (Generalized EM,  k-Means & Tree) options. 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.