A density-based competitive data stream clustering network with self-adaptive distance metric. Xu, B., Shen, F., & Zhao, J. Neural Networks, 110:141–158, February, 2019. ![A density-based competitive data stream clustering network with self-adaptive distance metric [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Data stream clustering is a branch of clustering where patterns are processed as an ordered sequence. In this paper, we propose an unsupervised learning neural network named Density Based Self Organizing Incremental Neural Network(DenSOINN) for data stream clustering tasks. DenSOINN is a self organizing competitive network that grows incrementally to learn suitable nodes to fit the distribution of learning data, combining online unsupervised learning and topology learning by means of competitive Hebbian learning rule. By adopting a density-based clustering mechanism, DenSOINN discovers arbitrarily shaped clusters and diminishes the negative effect of noise. In addition, we adopt a self-adaptive distance framework to obtain good performance for learning unnormalized input data. Experiments show that the DenSOINN can achieve high standard performance comparing to state-of-the-art methods.
@article{xu_density-based_2019,
title = {A density-based competitive data stream clustering network with self-adaptive distance metric},
volume = {110},
issn = {0893-6080},
url = {https://www.sciencedirect.com/science/article/pii/S0893608018303307},
doi = {10.1016/j.neunet.2018.11.008},
abstract = {Data stream clustering is a branch of clustering where patterns are processed as an ordered sequence. In this paper, we propose an unsupervised learning neural network named Density Based Self Organizing Incremental Neural Network(DenSOINN) for data stream clustering tasks. DenSOINN is a self organizing competitive network that grows incrementally to learn suitable nodes to fit the distribution of learning data, combining online unsupervised learning and topology learning by means of competitive Hebbian learning rule. By adopting a density-based clustering mechanism, DenSOINN discovers arbitrarily shaped clusters and diminishes the negative effect of noise. In addition, we adopt a self-adaptive distance framework to obtain good performance for learning unnormalized input data. Experiments show that the DenSOINN can achieve high standard performance comparing to state-of-the-art methods.},
language = {en},
urldate = {2021-10-18},
journal = {Neural Networks},
author = {Xu, Baile and Shen, Furao and Zhao, Jinxi},
month = feb,
year = {2019},
keywords = {Clustering methods, Competitive neural networks, Stream learning, Unsupervised learning},
pages = {141--158},
}
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