At training phase, I applied k-means clustering algorithm on a dataset (k=10). I want to apply decision tree on each cluster and generate a separate performance model for each cluster
At testing phase, I want to compare each test instance with the centroid of each cluster and use appropriate model to classify the instance.
Is there a way I can achieve this in WEKA or WEKA API. Any link/resource will be appreciated
Hi Johnson,
Several ways to do that; for example, you can use WEKA's KnowledgeFlow as follows:
Training set will be passed into SimpleKMeans, after that prediction model gets built using J48 based on the generated clusters. All this process is evaluated using cross-validation.
NB: The attached image shows the flow process where FilteredClassifier is containing both cluster and classification algorithms.
HTH.
Samer
Thanks Samer Sarsam. Please, I will let you know if I have any issue while trying this out.
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