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Enriched topological learning for cluster detection and visualization
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DOI：10.1016/j.neunet.2012.02.019.png)
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Abstract
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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. (C) 2012 Elsevier Ltd. All rights reserved.
Keywords:
Self-Organizing Map
Prototype enrichment
Two-level clustering
Coclustering
Visualization
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