Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
On computing correlated aggregates over continual data streams
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Analysing a Contingency Table with Kohonen Maps: A Factorial Correspondence Analysis
IWANN '93 Proceedings of the International Workshop on Artificial Neural Networks: New Trends in Neural Computation
Biclustering Algorithms for Biological Data Analysis: A Survey
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Approximate frequency counts over data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
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 framework for clustering evolving data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
A Simultaneous Two-Level Clustering Algorithm for Automatic Model Selection
ICMLA '07 Proceedings of the Sixth International Conference on Machine Learning and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
A two-level clustering method using linear linkage encoding
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Approximation Algorithms for Wavelet Transform Coding of Data Streams
IEEE Transactions on Information Theory
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The exponential growth of data generates terabytes of very large databases. The growing number of data dimensions and data objects presents tremendous challenges for effective data analysis and data exploration methods and tools. Thus, it becomes crucial to have methods able to construct a condensed description of the properties and structure of data, as well as visualization tools capable of representing the data structure from these condensed descriptions. The purpose of our work described in this paper is to develop a method of describing data from enriched and segmented prototypes using a topological clustering algorithm. We then introduce a visualization tool that can enhance the structure within and between groups in data. We show, using some artificial and real databases, the relevance of the proposed approach.