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• Part 1 The initial four cluster partition {c1, c2, c3, c4} for the text collection is provided by this link text vs cluster. Assuming that
  the ground-truth partition is given by
  1. cacm texts belong to cluster1
  2. cisi texts belong to cluster2
  3. cran texts belong to cluster3
  4. med texts belong to cluster4
  build the confusion matrix (CM) for the partition provided. That is build a 4X4 matrix whose row i shows distribution of ci elements
  among cluster1, cluster2, cluster3, and cluster4. For example the first row is computed as follows: CM₁₁=|c1∩cluster1|,  CM₁₂=|c1∩cluster2|,  CM₁₃=|c1∩cluster3|, and CM₁₄=|c1∩cluster4|

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• Part 2 Apply batch k-means to the partition {c1, c2, c3, c4}. Call the obtained partition {c1', c2', c3', c4'}.

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• Part 3 Build a the confusion matrix for the partition {c1', c2', c3', c4'}.

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OUTPUT: initial and final confusion matrices [Please upload a single file.]