Data clustering
QCanvas provides a total of eight popular measures for generating the similarity matrix-i.e., Correlation uncenter, Correlation center, Absolute corr-uncenter, Absolute corrcenter, Spearman rank, Kendall's tau, Euclidean distance, and City-block distance. All of these measures have typically been included among the data clustering methods of previous tools [4]. In QCanvas, the calculation of the similarity matrix is selectively applied to the data for the x-axis and the y-axis independently. Hierarchical clustering is simultaneously carried out based on the established similarity matrices. QCanvas provides diverse algorithms for hierarchical clustering, such as the average method, centroid method, single method, and complete method. QCanvas uses a standard window-based graphical user interface (GUI), providing multiple windows to comparatively visualize patterns of various combinations of similarity matrices and hierarchical clustering methods. This program provides quantitative trees for displaying clustering patterns and similarity measures together.