Mixture models of categorization
Journal of Mathematical Psychology
KI '99 Proceedings of the 23rd Annual German Conference on Artificial Intelligence: Advances in Artificial Intelligence
RBF Networks Exploiting Supervised Data in the Adaptation of Hidden Neuron Parameters
AI*IA 01 Proceedings of the 7th Congress of the Italian Association for Artificial Intelligence on Advances in Artificial Intelligence
A Neural Network Tool to Organize Large Document Sets
AIMSA '00 Proceedings of the 9th International Conference on Artificial Intelligence: Methodology, Systems, and Applications
Distributed unsupervised learning using the multisoft machine
Information Sciences—Informatics and Computer Science: An International Journal
Clustering binary data streams with K-means
DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Efficient Disk-Based K-Means Clustering for Relational Databases
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Knowledge and Data Engineering
Integrating K-Means Clustering with a Relational DBMS Using SQL
IEEE Transactions on Knowledge and Data Engineering
An adaptive incremental LBG for vector quantization
Neural Networks
Incremental Figure-Ground Segmentation Using Localized Adaptive Metrics in LVQ
WSOM '09 Proceedings of the 7th International Workshop on Advances in Self-Organizing Maps
Clustering: A neural network approach
Neural Networks
On the efficiency of swap-based clustering
ICANNGA'09 Proceedings of the 9th international conference on Adaptive and natural computing algorithms
Perspectives of self-adapted self-organizing clustering in organic computing
BioADIT'06 Proceedings of the Second international conference on Biologically Inspired Approaches to Advanced Information Technology
SOM and neural gas as graduated nonconvexity algorithms
ICCSA'06 Proceedings of the 2006 international conference on Computational Science and Its Applications - Volume Part III
Engineering Applications of Artificial Intelligence
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A new vector quantization method (LBG-U) closely related to a particular class of neural network models (growing self-organizingnetworks) is presented. LBG-U consists mainly of repeated runs of thewell-known LBG algorithm. Each time LBG converges, however, a novelmeasure of utility is assigned to each codebook vector. Thereafter, thevector with minimum utility is moved to a new location, LBG is run on theresulting modified codebook until convergence, another vector is moved, andso on. Since a strictly monotonous improvement of the LBG-generatedcodebooks is enforced, it can be proved that LBG-U terminates in a finitenumber of steps. Experiments with artificial data demonstrate significantimprovements in terms of RMSE over LBG combined with only modestly highercomputational costs.