lib/ai4r/clusterers/ward_linkage.rb in ai4r-1.12 vs lib/ai4r/clusterers/ward_linkage.rb in ai4r-1.13

@@ -14,24 +14,24 @@
   module Clusterers
      
     # Implementation of an Agglomerative Hierarchical clusterer with 
     # Ward's method linkage algorithm, aka the minimum variance method (Everitt
     # et al., 2001 ; Jain and Dubes, 1988 ; Ward, 1963 ).
-    # Hierarchical clusteres create one cluster per element, and then 
+    # Hierarchical clusterer create one cluster per element, and then 
     # progressively merge clusters, until the required number of clusters
     # is reached.
-    # The objective of this method is to minime the variance. 
+    # The objective of this method is to minimize the variance. 
     # 
     #   D(cx, (ci U cj)) =  (ni/(ni+nj+nx))*D(cx, ci) + 
     #                       (nj/(ni+nj+nx))*D(cx, cj) - 
     #                       (nx/(ni+nj)^2)*D(ci, cj)
     class WardLinkage < SingleLinkage
       
     parameters_info :distance_function => 
           "Custom implementation of distance function. " +
           "It must be a closure receiving two data items and return the " +
-          "distance bewteen them. By default, this algorithm uses " + 
-          "ecuclidean distance of numeric attributes to the power of 2."
+          "distance between them. By default, this algorithm uses " + 
+          "euclidean distance of numeric attributes to the power of 2."
       
       # Build a new clusterer, using data examples found in data_set.
       # Items will be clustered in "number_of_clusters" different
       # clusters.
       def build(data_set, number_of_clusters)