ee.Clusterer.wekaLVQ
A Clusterer that implements the Learning Vector Quantization algorithm. For more details, see:
T. Kohonen, "Learning Vector Quantization", The Handbook of Brain Theory and Neural Networks, 2nd Edition, MIT Press, 2003, pp. 631-634.
| Usage | Returns |
|---|
ee.Clusterer.wekaLVQ(numClusters, learningRate, epochs, normalizeInput) | Clusterer |
| Argument | Type | Details |
|---|
numClusters | Integer, default: 7 | The number of clusters. |
learningRate | Float, default: 1 | The learning rate for the training algorithm. Value should be greater than 0 and less or equal to 1. |
epochs | Integer, default: 1000 | Number of training epochs. Value should be greater than or equal to 1. |
normalizeInput | Boolean, default: false | Skip normalizing the attributes. |
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Last updated 2024-09-19 UTC.
[null,null,["Last updated 2024-09-19 UTC."],[[["Implements the Learning Vector Quantization (LVQ) algorithm for clustering data."],["Users can specify the desired number of clusters, learning rate, training epochs, and input normalization."],["Based on the Kohonen's work as described in \"The Handbook of Brain Theory and Neural Networks\"."],["The algorithm learns by adjusting cluster prototypes based on the input data during training epochs."],["It returns a Clusterer object that can be used to predict the cluster assignments for new data points."]]],["The `ee.Clusterer.wekaLVQ` function implements the Learning Vector Quantization algorithm for clustering. It requires specifying the number of clusters (`numClusters`, default 7), the learning rate (`learningRate`, default 1, between 0 and 1), the number of training epochs (`epochs`, default 1000, at least 1), and whether to normalize the input attributes (`normalizeInput`, default false). The function returns a Clusterer object. The algorithm's details are described in a specific paper by T. Kohonen.\n"]]