Core zone scatterplots: a new approach to feature extraction for visual displays
Computer Vision, Graphics, and Image Processing
A computer generated aid for cluster analysis
Communications of the ACM
Co-clustering documents and words using bipartite spectral graph partitioning
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Self-Organizing Maps
Computer Solution of Large Sparse Positive Definite
Computer Solution of Large Sparse Positive Definite
Path Based Pairwise Data Clustering with Application to Texture Segmentation
EMMCVPR '01 Proceedings of the Third International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition
Visual cluster validity for prototype generator clustering models
Pattern Recognition Letters
Mathematical concepts and novel heuristic methods for data clustering and visualization
Mathematical concepts and novel heuristic methods for data clustering and visualization
Information-theoretic co-clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Minimum sum-squared residue for fuzzy co-clustering
Intelligent Data Analysis
Scalable visual assessment of cluster tendency for large data sets
Pattern Recognition
A Nonlinear Mapping for Data Structure Analysis
IEEE Transactions on Computers
A Generalized Maximum Entropy Approach to Bregman Co-clustering and Matrix Approximation
The Journal of Machine Learning Research
A heuristic-based fuzzy co-clustering algorithm for categorization of high-dimensional data
Fuzzy Sets and Systems
Is VAT really single linkage in disguise?
Annals of Mathematics and Artificial Intelligence
Dual fuzzy-possibilistic coclustering for categorization of documents
IEEE Transactions on Fuzzy Systems
Clustering ellipses for anomaly detection
Pattern Recognition
ICAISC'10 Proceedings of the 10th international conference on Artificial intelligence and soft computing: Part I
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
An Efficient Formulation of the Improved Visual Assessment of Cluster Tendency (iVAT) Algorithm
IEEE Transactions on Knowledge and Data Engineering
Visual Assessment of Clustering Tendency for Rectangular Dissimilarity Matrices
IEEE Transactions on Fuzzy Systems
| Hi-index | 0.00 |
Since 1998, a graphical representation used in visual clustering called the reordered dissimilarity image or cluster heat map has appeared in more than 4000 biological or biomedical publications. These images are typically used to visually estimate the number of clusters in a data set, which is the most important input to most clustering algorithms, including the popularly chosen fuzzy c-means and crisp k-means. This paper presents a new formulation of a matrix reordering algorithm, coVAT, which is the only known method for providing visual clustering information on all four types of cluster structure in rectangular relational data. Finite rectangular relational data are an m× n array R of relational values between m row objects Or and n column objects Oc. R presents four clustering problems: clusters in Or, Oc, Or∪c, and coclusters containing some objects from each of Or and Oc. coVAT1 is a clustering tendency algorithm that provides visual estimates of the number of clusters to seek in each of these problems by displaying reordered dissimilarity images. We provide several examples where coVAT1 fails to do its job. These examples justify the introduction of coVAT2, a modification of coVAT1 based on a different reordering scheme. We offer several examples to illustrate that coVAT2 may detect coclusters in R when coVAT1 does not. Furthermore, coVAT2 is not limited to just relational data R. The R matrix can also take the form of feature data, such as gene microarray data where each data element is a real number: Positive values indicate upregulation, and negative values indicate downregulation. We show examples of coVAT2 on microarray data that indicate coVAT2 shows cluster tendency in these data. © 2012 Wiley Periodicals, Inc. © 2012 Wiley Periodicals, Inc.