Recurrent Self Organizing Maps in Encog for Unsupervised Clustering with Context

Question

Machine Learning - what a hoot!

I have a little project with which I would like to identify anomalies in unlabeled data. Thus, unsupervised clustering.

However, the sequence of the data is also important, as a single record may not be of interest, but the sequence of records that precede it may make it anomalous.

So I am thinking of building a Recurrent SOM to add the temporal context.

I have trained a few simple Machine Learning Models using Python Graphlab Create, Azure Machine Learning and Encog ML Framework, but Azure does not seem to provide unsupervised clustering and I am leaning towards using Encog.

I have looked at Recurrent Neural Networks in Encog, as well as SOM, but I have no idea how to combine the two. Most of the articles online regarding Feedback/Recurrent SOM Machine Learning are mostly academic.

Are there any good references for doing this with Encog?

Answer 1

谷歌搜索在Encog中仅找到RSOM的一个很好的答案： https : //github.com/leadtune/encog-java/blob/master/encog-core/src/org/encog/neural/pattern/RSOMPattern.java