Self-Organizing Maps
Clustering quality measures for data samples with multiple labels
DBA'06 Proceedings of the 24th IASTED international conference on Database and applications
Novel labeling strategies for hierarchical representation of multidimensional data analysis results
AIA '08 Proceedings of the 26th IASTED International Conference on Artificial Intelligence and Applications
Feature-based cluster validation for high-dimensional data
AIA '08 Proceedings of the 26th IASTED International Conference on Artificial Intelligence and Applications
IEA/AIE'11 Proceedings of the 24th international conference on Industrial engineering and other applications of applied intelligent systems conference on Modern approaches in applied intelligence - Volume Part I
WSOM'11 Proceedings of the 8th international conference on Advances in self-organizing maps
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Neural clustering algorithms show high performance in the usual context of the analysis of homogeneous textual dataset. This is especially true for the recent adaptive versions of these algorithms, like the incremental neural gas algorithm (IGNG). Nevertheless, this paper highlights clearly the drastic decrease of performance of these algorithms, as well as the one of more classical algorithms, when a heterogeneous textual dataset is considered as an input. A new incremental growing neural gas algorithm exploiting knowledge issued from clusters current labeling in an incremental way is proposed as an alternative to the original distance based algorithm. This solution leads to obtain very significant increase of performance for the clustering of heterogeneous textual data. Moreover, it provides a real incremental character to the proposed algorithm.