Topology representing networks
Neural Networks
Dynamic cell structure learns perfectly topology preserving map
Neural Computation
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
ACM Computing Surveys (CSUR)
An empirical comparison of four initialization methods for the K-Means algorithm
Pattern Recognition Letters
A Study on the Hierarchical Data Clustering Algorithm Based on Gravity Theory
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
A robust and efficient clustering algorithm based on cohesion self-merging
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
`Neural-gas' network for vector quantization and its application to time-series prediction
IEEE Transactions on Neural Networks
A supervised growing neural gas algorithm for cluster analysis
International Journal of Hybrid Intelligent Systems
A supervised growing neural gas algorithm for cluster analysis
International Journal of Hybrid Intelligent Systems
Pattern-Based Reasoning System Using Self-incremental Neural Network for Propositional Logic
Neural Information Processing
Active-GNG: model acquisition and tracking in cluttered backgrounds
VNBA '08 Proceedings of the 1st ACM workshop on Vision networks for behavior analysis
Debellor: A Data Mining Platform with Stream Architecture
Transactions on Rough Sets IX
An Online Incremental Learning Vector Quantization
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Clustering: A neural network approach
Neural Networks
An online incremental learning pattern-based reasoning system
Neural Networks
Reinforcement learning and adaptive dynamic programming for feedback control
IEEE Circuits and Systems Magazine
An Associated-Memory-Based Stock Price Predictor
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
Mobile robot vision-based navigation using self-organizing and incremental neural networks
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Incremental clustering of gesture patterns based on a self organizing incremental neural network
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Growing self-reconstruction maps
IEEE Transactions on Neural Networks
Classification of temporal data based on self-organizing incremental neural network
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Nonparametric modelling and tracking with active-GNG
HCI'07 Proceedings of the 2007 IEEE international conference on Human-computer interaction
A common-neural-pattern based reasoning for mobile robot cognitive mapping
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
TopoART: a topology learning hierarchical ART network
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part III
Continuous visual codebooks with a limited branching tree growing neural gas
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part III
How to use the SOINN software: user's guide (version 1.0)
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part III
Self-organizing incremental neural network and its application
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part III
Machine learning approaches for time-series data based on self-organizing incremental neural network
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part III
Online knowledge acquisition and general problem solving in a real world by humanoid robots
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part III
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part III
An online incremental learning support vector machine for large-scale data
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part II
A multidirectional associative memory based on self-organizing incremental neural network
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part I
Neurocomputing
Growing graph network based on an online gaussian mixture model
WSOM'11 Proceedings of the 8th international conference on Advances in self-organizing maps
An extended TopoART network for the stable on-line learning of regression functions
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
GPGPU implementation of growing neural gas: Application to 3D scene reconstruction
Journal of Parallel and Distributed Computing
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
A local distribution net for data clustering
PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
Self-organizing maps with a time-varying structure
ACM Computing Surveys (CSUR)
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This paper presents an on-line unsupervised learning mechanism for unlabeled data that are polluted by noise. Using a similarity threshold-based and a local error-based insertion criterion, the system is able to grow incrementally and to accommodate input patterns of on-line non-stationary data distribution. A definition of a utility parameter, the error-radius, allows this system to learn the number of nodes needed to solve a task. The use of a new technique for removing nodes in low probability density regions can separate clusters with low-density overlaps and dynamically eliminate noise in the input data. The design of two-layer neural network enables this system to represent the topological structure of unsupervised on-line data, report the reasonable number of clusters, and give typical prototype patterns of every cluster without prior conditions such as a suitable number of nodes or a good initial codebook.