Clustering of the self-organizing map

By Juha Vesanto, Esa Alhoniemi. IEEE Trans. Neural Networks Learn. Syst., 2000

2,807 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/931521da1f1b8f4ed47d638e1747a87f38506db4

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Citations in the corpus

Survey of clustering algorithms (2005, neutral): “Use crossover (two-point crossover) and mutation (bit-wise mutation) operator to generate offspring from the individuals chosen in step 2).”
Reservoir-system simulation and optimization techniques (2013, methodology): “In addition, the combination of the constraint technique, decomposition iteration and simulation analysis as a single methodology by Wang et al. (2005) was presented to reduce the problem of dimensionality in providing solution to the stochastic multiobjective optimization problem of reservoirs in p”
A Survey on ensemble learning under the era of deep learning (2021, methodology): “For generating more diverse base learners, the training set were divided into three subsets using clustering of self-organizing map (Vesanto and Alhoniemi 2000) and K-fold cross-validation (Bishop 2006).”
A review of clustering techniques and developments (2017, neutral): “SOM is commonly used for vector quantization, feature extraction and data visualization along with clustering analysis.”
Color image segmentation: advances and prospects (2001, background): “The self-organizing map (SOM) The self-organizing map (SOM) projects input space on prototypes of low-dimensional regular grids that can be e!ectively utilized to visualize and explore properties of the data [127].”
Self-Organizing Feature Maps for Modeling and Control of Robotic Manipulators (2003, background): “Most works in SOFMs have focused on robotic systems that are solely sensory in nature, being applied to clustering of these data (Vesanto and Alhoniemi, 2000).”
A Novel Chemometric Method for the Prediction of Human Oral Bioavailability (2012, background): “representation of the input space of the training samples [30].”
Damage classification in carbon fibre composites using acoustic emission: A comparison of three techniques (2015, methodology): “Train a self-organising map (SOM) with AE parameters in unsupervised mode [17].”
Clustering of interlaminar and intralaminar damages in laminated composites under indentation loading using Acoustic Emission (2018, positive): “When a data is fed into the network, the similarity of the data with the weight vector of all neurons is checked and ﬁ nally, the weight vectors of the winning neuron ( W v ) and its neighborhood neurons are updated by the following equation [41]:”
Review of the self-organizing map (SOM) approach in water resources: Analysis, modelling and application (2008, methodology): “…map) is a kind of ANN method which is capable of clustering, classification, estimation, prediction, and data mining (Alhoniemi et al., 1999; Vesanto and Alhoniemi, 2000; Kohonen, 2001) in a wide-spread range of disciplines regarding signal recognition, organization of large collections of…”
Size & Regional Distribution of Financial Behavior Patterns in Spain (2010, background): “Although its roots dates from the 60s (Minsky & Papert, 1969; Rosenblatt, 1958; Wildrow & Hoff, 1960)), they really appeared as an alternative to the predominant boolean logic computation during the 90s (Hopfield, 1984; Kohonen, 1982; Vesanto & Alhoniemi, 2000).”