Clustering Algorithms
Data Mining and Knowledge Discovery
Large-scale data exploration with the hierarchically growing hyperbolic SOM
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
A Nonlinear Mapping for Data Structure Analysis
IEEE Transactions on Computers
Finding Prototypes For Nearest Neighbor Classifiers
IEEE Transactions on Computers
Neural Computing and Applications
Scalable dynamic self-organising maps for mining massive textual data
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
Clustering massive high dimensional data with dynamic feature maps
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
Dynamic self-organizing maps with controlled growth for knowledge discovery
IEEE Transactions on Neural Networks
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Self-Organizing Map (SOM) and Growing Self-Organizing Map (GSOM) are widely used techniques for exploratory data analysis. The key desirable features of these techniques are applicability to real world data sets and the ability to visualize high dimensional data in low dimensional output space. One of the core problems of using SOM/GSOM based techniques on large datasets is the high processing time requirement. A possible solution is the generation of multiple maps for subsets of data where the subsets consist of the entire dataset. However the advantage of topographic organization of a single map is lost in the above process. This paper proposes a new technique where Sammon's projection is used to merge an array of GSOMs generated on subsets of a large dataset. We demonstrate that the accuracy of clustering is preserved after the merging process. This technique utilizes the advantages of parallel computing resources.