Elements of information theory
Elements of information theory
Complexity optimized data clustering by competitive neural networks
Neural Computation
Self-organizing maps
A Robust Competitive Clustering Algorithm With Applications in Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Algorithms for Model-Based Gaussian Hierarchical Clustering
SIAM Journal on Scientific Computing
Neural Networks and Simulation Methods
Neural Networks and Simulation Methods
Clustering Algorithms
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Bayesian Clustering by Dynamics
Machine Learning - Special issue: Unsupervised learning
Web mining for web personalization
ACM Transactions on Internet Technology (TOIT)
A unified framework for model-based clustering
The Journal of Machine Learning Research
Self-splitting competitive learning: a new on-line clustering paradigm
IEEE Transactions on Neural Networks
A scalable framework for cluster ensembles
Pattern Recognition
Optimizing zero-slice feature of ambiguity function for radar emitter identification
ICICS'09 Proceedings of the 7th international conference on Information, communications and signal processing
Masking of time-frequency patterns in applications of passive underwater target detection
EURASIP Journal on Advances in Signal Processing - Special issue on advances in signal processing for maritime applications
Expert Systems with Applications: An International Journal
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Radar emitter classification is a special application of data clustering for classifying unknown radar emitters from received radar pulse samples. The main challenges of this task are the high dimensionality of radar pulse samples, small sample group size, and closely located radar pulse clusters. In this paper, two new online clustering algorithms are developed for radar emitter classification: One is model-based using the Minimum Description Length (MDL) criterion and the other is based on competitive learning. Computational complexity is analyzed for each algorithm and then compared. Simulation results show the superior performance of the model-based algorithm over competitive learning in terms of better classification accuracy, flexibility, and stability.