A computer generated aid for cluster analysis

Abstract

A computer generated graphic method, which can be used in conjunction with any hierarchical scheme of cluster analysis, is described and illustrated. The graphic principle used is the representation of the elements of a data matrix of similarities or dissimilarities by computer printed symbols (of character overstrikes) of various shades of darkness, where a dark symbol corresponds to a small dissimilarity. The plots, applied to a data matrix before clustering and to the rearranged matrix after clustering, show at a glance whether clustering brought forth any distinctive clusters. A well-known set of data consisting of the correlations of 24 psychological tests is used to illustrate the comparison of groupings by four methods of factor analysis and two methods of cluster analysis.

Linking card sorting to browsing performance – are congruent municipal websites more efficient to use?

“…These regions can readily be interpreted as semantic clusters. Manually re-arranging items can become extremely tedious for larger sets, but can be automated by recording the order of iterative aggregation steps during HCA and imposing this order onto the similarity matrix (Ling 1973). Compared to dendrograms, ordered heatmaps have the particular advantage of conveying the full information on similarity.…”

The History of the Cluster Heat Map

“…Sneath (1957) was perhaps the earliest advocate for this graphic. Ling (1973) introduced a computer program, called SHADE, for implementing Sneath's idea. Ling's program used overstrikes on a character printer to represent different degrees of shading.…”

“…Hierarchical clustering algorithms do not determine a particular Figure 7. Permuted cluster display from Gower and Digby (1981), following Ling (1973). This display was designed to represent a symmetric similarity/dissimilarity matrix.…”

Anti-Inflammatory Effect and Chemical Composition of Bupleurum chinense and Bupleurum kaoi

“…9 spots were chosen for cluster analysis. Cluster analysis: heat map [10,11] was applied to evaluate the overall features of TLC fingerprint among the 10 batches of Bupleurum samples, and heat map-based feature selection was then used to reduce the number of features and to find impact factor of them. The greater of variance in the same group of the heat map cluster result was chosen and repeated this steps until the cluster result was changed.…”