I want to integrate user input into clustering algorithm. So that users can control which docs should be in the same cluster. What are some ways to achieve this? Can metric learning do this?


closed as unclear what you're asking by ttnphns, gung, Matt Krause, Nick Cox, COOLSerdash Feb 11 '14 at 18:31

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  • $\begingroup$ This question leaves so scarce information so far. What clustering method do you conceive of? What is the field of research? Tell more about the constraint you want to integrate. $\endgroup$ – ttnphns Feb 11 '14 at 16:08
  • $\begingroup$ @ttnphnns I want to get an impression of which type of method in general are there. $\endgroup$ – gstar2002 Feb 11 '14 at 16:26

If you mean that users get to give some pairwise constraints on what items should/shouldn't be in the same cluster, that's called constrained clustering (see wikipedia page; apparently I don't have enough "reputation" to post more than two links...).

The following older question seems relevant: Supervised clustering or classification?

As for doing this in an interactive manner, i.e. updating the clustering (quickly, i.e. without too much computation) as individual constraints are given by the user, the following two papers might be of interest:

First: Xiong, Johnson, and Corso (2012)

Second: "A Semi-supervised Incremental Clustering Algorithm for Streaming Data" Maria Halkidi, Myra Spiliopoulou, Aikaterini Pavlou (2012)


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