úÎ+K*?    1(c) Alp Mestanogullari, Ville Tirronen, 2011-2015BSD3(Alp Mestanogullari <alpmestan@gmail.com> experimentalNone.B A # of points is just a list of points$elements that belong to that cluster This type is used internally by  #. It represents our (hopefully) k9 centroids, obtained by computing the new centroids of a  This is what   hands you back. It's just a  1 of clusters that will hopefully be of length k.A distance on vectors…The euclidean distance without taking the final square root This would waste cycles without changing the behavior of the algorithmAL1 distance of two vectors: d(v1, v2) = sum on i of |v1_i - v2_i|<L-inf distance of two vectors: d(v1, v2) = max |v1_i - v2_i] 6This is the current partitionning strategy used by   . If we want k> clusters, we just try to regroup consecutive elements in k buckets $Run the kmeans clustering algorithm. kmeans f distance k pointswill run the algorithm using f, to extract features from your type, using distance> to measure the distance between vectors, trying to separate points in k clusters.)Extracting features just means getting a   with B coordinates that will represent your type in the space in which   will run. Same as  , except that instead of using  d, you supply your own function for choosing the initial clustering. Two important things to note:5If you don't need any kind of effect and just have a  %-like function you want to use, m will can just be m here. If that's too obnoxious to work with, please let me know and I may just provide a separate  q function with no there. But most of the time, you'll probably just be interested in the following scenario.@Most likely, you want to have something smarter than our simple  ¬ function. A couple of papers I have read claim very decent results by using some precise probabilistic schemas for the initial partitionning. In this case, your m would probably be  or ST (e.g using my  +http://hackage.haskell.org/package/probableprobableà package) and you could fine-tune the way the initial clusters are picked so that the algorithm may give better results. Of course, if your initialization is monadic, so is the result.  feature extractiondistance functionthe k to run k,-means with (i.e number of desired clusters)input list of pointsresult, hopefully k clusters of points #how should we partition the points? get the coordinates of a "point"what distance do we usenumber of desired clusterslist of pointsresulting clustering          kmeans-vector-0.3.2 Math.KMeansClusterelements CentroidsClustersDistanceeuclidSql1distlinfdist partitionkmeans kmeansWithvector-0.10.12.3 Data.VectorVectorData.Vector.Unboxed.Baseghc-prim GHC.TypesDoubletransformers-0.3.0.0Data.Functor.IdentityIdentityIO clusterAdd emptyCluster addCentroids