cluster-analysisk-meansgensimdoc2vecelki

ELKI Kmeans clustering Task failed error for high dimensional data

I have a 60000 documents which i processed in gensim and got a 60000*300 matrix. I exported this as a csv file. When i import this in ELKI environment and run Kmeans clustering, i am getting below error.

Task failed
de.lmu.ifi.dbs.elki.data.type.NoSupportedDataTypeException: No data type found satisfying: NumberVector,field AND NumberVector,variable
Available types: DBID DoubleVector,variable,mindim=266,maxdim=300 LabelList
    at de.lmu.ifi.dbs.elki.database.AbstractDatabase.getRelation(AbstractDatabase.java:126)
    at de.lmu.ifi.dbs.elki.algorithm.AbstractAlgorithm.run(AbstractAlgorithm.java:81)
    at de.lmu.ifi.dbs.elki.workflow.AlgorithmStep.runAlgorithms(AlgorithmStep.java:105)
    at de.lmu.ifi.dbs.elki.KDDTask.run(KDDTask.java:112)
    at de.lmu.ifi.dbs.elki.application.KDDCLIApplication.run(KDDCLIApplication.java:61)
    at [...]

Below is the ELKI settings i have used enter image description here

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Solution

  • This sounds strange, but i found the solution to this issue by opening the exported CSV file and doing Save As and saving again as a CSV file. While size of the original file is 437MB, the second file is 163MB. I used the numpy function np.savetxt for saving the doc2vec vector. So it seems to be a Python issue instead of being ELKI issue.

    Edit: Above solution is not useful. I instead exported the doc2vec output which was created using gensim library and while exporting format of the values were decided explicitly as %1.22e. i.e. the values exported are in exponential format and values have length of 22. Below is the entire line of code.

    textVect = model.docvecs.doctag_syn0
np.savetxt('D:\Backup\expo22.csv',textVect,delimiter=',',fmt=('%1.22e'))

    CSV file thus created runs without any issue in ELKI environment.