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Table 1 The thematic clustering algorithm

From: Thematic clustering of text documents using an EM-based approach

Given K initial clusters, the number n U , and the set of prior probabilities {pr d }dD,
   1. Create a random partition { V i } i = 1 K of D with corresponding relations { R i } i = 1 K .
   2. Compute p t , q t , and r t for V i .
   3. Compute α t for V i .
   4. For each cluster, select the n U points for which α t is the greatest to define the set U and the indicator values {u t }tT.
   5. Compute the probabilities {pz d }dDfor each cluster V i .
   6. For all d, assign a document to the cluster in which the document has the highest probability.
   7. Test for convergence. Terminate if converged.
   8. For a subset D s D V i , where the documents in D s has the lowest 1% {pz d } in V i , re-assign to the clusters that have the second highest probabilities.
   9. Return to Step 2.