kohonennode properties

Kohonen node iconThe Kohonen node generates a type of neural network that can be used to cluster the data set into distinct groups. When the network is fully trained, records that are similar should be close together on the output map, while records that are different will be far apart. You can look at the number of observations captured by each unit in the model nugget to identify the strong units. This may give you a sense of the appropriate number of clusters.


node = stream.create("kohonen", "My node")
# "Model" tab
node.setPropertyValue("use_model_name", False)
node.setPropertyValue("model_name", "Symbolic Cluster")
node.setPropertyValue("stop_on", "Time")
node.setPropertyValue("time", 1)
node.setPropertyValue("set_random_seed", True)
node.setPropertyValue("random_seed", 12345)
node.setPropertyValue("optimize", "Speed")
# "Expert" tab
node.setPropertyValue("mode", "Expert")
node.setPropertyValue("width", 3)
node.setPropertyValue("length", 3)
node.setPropertyValue("decay_style", "Exponential")
node.setPropertyValue("phase1_neighborhood", 3)
node.setPropertyValue("phase1_eta", 0.5)
node.setPropertyValue("phase1_cycles", 10)
node.setPropertyValue("phase2_neighborhood", 1)
node.setPropertyValue("phase2_eta", 0.2)
node.setPropertyValue("phase2_cycles", 75)
Table 1. kohonennode properties
kohonennode Properties Values Property description
inputs [field1 ... fieldN] Kohonen models use a list of input fields, but no target. Frequency and weight fields are not used. See Common modeling node properties for more information.
continue flag  
show_feedback flag  
time number  
Use to specify whether model building should be optimized for speed or for memory.
cluster_label flag  
width number  
length number  
phase1_neighborhood number  
phase1_eta number  
phase1_cycles number  
phase2_neighborhood number  
phase2_eta number  
phase2_cycles number  
set_random_seed Boolean If no random seed is set, the sequence of random values used to initialize the network weights will be different every time the node runs. This can cause the node to create different models on different runs, even if the node settings and data values are exactly the same. By selecting this option, you can set the random seed to a specific value so the resulting model is exactly reproducible.
random_seed integer Seed