MOA is an open source framework for Big Data stream mining. It includes a collection of machine learning algorithms (classification, regression, clustering, outlier detection, concept drift detection and recommender systems) and tools for evaluation.
Is there any documentation on how to convert vectors into Instances, I see that all clusteres expect "Instance" instances not array of numbers, So I was wondering how to generate the header also what is classIndex, classValue. in my case the ground truth clusters are unknown.
provided that I have some vectors (Not one of the supported stream generators) without ground truth clusters, Could you provide an example for how to use any of the available clusterers?
Regarding your first question about creating Instances. You can look at SRP prepareRandomSubspaceInstance(...) which creates a new instance from an existing Instance.
Is there any documentation on how to convert vectors into Instances, I see that all clusteres expect "Instance" instances not array of numbers, So I was wondering how to generate the header also what is classIndex, classValue. in my case the ground truth clusters are unknown.
provided that I have some vectors (Not one of the supported stream generators) without ground truth clusters, Could you provide an example for how to use any of the available clusterers?
Thanks