The visual display of information in an information retrieval environment
Proceedings of the 9th annual international ACM SIGIR conference on Research and development in information retrieval
Visual cluster validity for prototype generator clustering models
Pattern Recognition Letters
Using data images for outlier detection
Computational Statistics & Data Analysis - Data visualization
Scalable visual assessment of cluster tendency for large data sets
Pattern Recognition
Semantic clustering: Identifying topics in source code
Information and Software Technology
A method of relational fuzzy clustering based on producing feature vectors using FastMap
Information Sciences: an International Journal
GAP: A graphical environment for matrix visualization and cluster analysis
Computational Statistics & Data Analysis
Permutation clustering using the proximity matrix
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
bigVAT: Visual assessment of cluster tendency for large data sets
Pattern Recognition
Clustering ellipses for anomaly detection
Pattern Recognition
Proceedings of the 3rd BELIV'10 Workshop: BEyond time and errors: novel evaLuation methods for Information Visualization
iVAT and aVAT: enhanced visual analysis for cluster tendency assessment
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
International Journal of Intelligent Systems
Employing heat maps to mine associations in structured routine care data
Artificial Intelligence in Medicine
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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.