Matrix Visualization and Information Mining

Many statistical techniques, particularly multivariate methodologies, focus on extracting information from data and proximity matrices. Rather than rely solely on numerical characteristics, matrix visualization allows one to graphically reveal structure in a matrix.This article reviews the history of matrix visualization, then gives a more detailed description of its general framework, along with some extensions. Possible research directions in matrix visualization and information mining are sketched. Color versions of figures presented in this article, together with software packages, can be obtained from http://gap.stat.sinica.edu.tw/.

[1]  William C. Halperin,et al.  Unclassed matrix shading and optimal ordering in hierarchical cluster analysis , 1984 .

[2]  James R. Slagle,et al.  A Clustering and Data-Reorganizing Algorithm , 1975, IEEE Transactions on Systems, Man, and Cybernetics.

[3]  Jan Karel Lenstra,et al.  Technical Note - Clustering a Data Array and the Traveling-Salesman Problem , 1974, Oper. Res..

[4]  Teuvo Kohonen,et al.  Self-Organizing Maps , 2010 .

[5]  J. Gower.  A General Coefficient of Similarity and Some of Its Properties , 1971 .

[6]  Tommi S. Jaakkola,et al.  Fast optimal leaf ordering for hierarchical clustering , 2001, ISMB.

[7]  Yueh-Yun Chi,et al.  Symptom patterns and subgrouping of schizophrenic patients: significance of negative symptoms assessed on admission , 2002, Schizophrenia Research.

[8]  F. Marcotorchino,et al.  Seriation problems: An overview , 1991 .

[9]  Robert F. Ling,et al.  A computer generated aid for cluster analysis , 1973, CACM.

[10]  M. Friendly.  Corrgrams , 2002 .

[11]  S. Kay,et al.  The positive and negative syndrome scale (PANSS) for schizophrenia. , 1987, Schizophrenia bulletin.

[12]  David J. Marchette,et al.  Using data images for outlier detection , 2003, Comput. Stat. Data Anal..

[13]  W. S. Robinson.  A Method for Chronologically Ordering Archaeological Deposits , 1951, American Antiquity.

[14]  Victor Chepoi,et al.  Recognition of Robinsonian dissimilarities , 1997 .

[15]  John W. Tukey,et al.  Exploratory Data Analysis. , 1979 .

[16]  J. Hartigan.  Direct Clustering of a Data Matrix , 1972 .

[17]  Ker-Chau Li,et al.  Sliced Inverse Regression for Dimension Reduction , 1991 .

[18]  L. Hubert.  SERIATION USING ASYMMETRIC PROXIMITY MEASURES , 1976 .

[19]  Chun-Houh Chen.  GENERALIZED ASSOCIATION PLOTS: INFORMATION VISUALIZATION VIA ITERATIVELY GENERATED CORRELATION MATRICES , 2002 .

[20]  D. Botstein,et al.  Cluster analysis and display of genome-wide expression patterns. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[21]  Ker-Chau Li.  Sliced inverse regression for dimension reduction (with discussion) , 1991 .

[22]  Yueh-Yun Chi,et al.  Relativity and Resolution for High Dimensional Information Visualization with Generalized Association Plots (GAP) , 2002, COMPSTAT.

[23]  Kenneth Ward Church,et al.  Dotplot : a program for exploring self-similarity in millions of lines of text and code , 1993 .

[24]  Michael Friendly,et al.  Effect ordering for data displays , 2003, Comput. Stat. Data Anal..

[25]  Trevor F. Cox,et al.  A General Weighted Two-Way Dissimilarity Coefficient , 2000, J. Classif..

[26]  R. Fisher.  THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS , 1936 .

[27]  E. Wegman.  Hyperdimensional Data Analysis Using Parallel Coordinates , 1990 .

[28]  D. J. Murdoch,et al.  A Graphical Display of Large Correlation Matrices , 1996 .