[Delayed] Neural net shuffles elements between clusters
Hello,
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.
Example:
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?
Greetings,
Thomas
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?
