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[Delayed] Neural net shuffles elements between clusters
i face the following problem:
I did some clustering and now i have about 1.700 data sets that belong to serveral clusters (cluster_0, cluster_1, ..., cluster_18).
I have additional 44 data sets that should by classified. To classify them a neural net should learn the 1.700 data sets above (cluser is the label-attribute). The neural net works good, so far but there is a major problem: similar elements are grouped into the same cluster, but the cluster itself seems to be the false one. To verify if this is a general problem, i told the neural net to learn from the great data set (1.700 examples) and classify the same 1.700 elements when the net was constructed.
The training data may be as followed (capital letters represent elements):
cluster_0: A, B, C, D
cluster_10: E, F, G, H
cluster_15: I, J, K, L
When the generated model of the neural net is applied on the same data that were used to train the net the results are for example:
cluster_0:E, F, G, H
cluster_10: I, J, K, L
cluster_15: A, B, C, D
... the elements are grouped together (fine!) but not into the right group (not fine!).
Anyone knows how to solve this problem?
PS: I would like to post the process i use, but my message would exeed the maximum of 20000 cahrakters. Is the whole process needed or should only some parts of the process do it?