{"head":"83bdca03b0d4cece7e276bd66c1bedb60a30f4da","size":3,"push_id":45336132,"shas":[["c0ab2f42a74bd9a62c7472a3779cb743b517aa78","800e7b83b5be3b953a6adf8f71d8bea9ef6354eb@gmail.com","::Proximity.squared_euclidian_distance only takes into account Numeric fields.","Gheorghita Catalin Bordianu"],["15009b393d077b9b9e140518ba541ee38f9af652","800e7b83b5be3b953a6adf8f71d8bea9ef6354eb@gmail.com","New module: ::Clusterers::Utils with a module level method: .within_cluster_sum_of_squares with returns the sum of the distances from all the points in a cluster to the cluster centroid.","Gheorghita Catalin Bordianu"],["83bdca03b0d4cece7e276bd66c1bedb60a30f4da","800e7b83b5be3b953a6adf8f71d8bea9ef6354eb@gmail.com","New module ::Clusterers::Indexes::CalinskiHarabaszIndex. Contains the implementation for an index which assesses how good does a given partition of dataset into clusters captures the real cluster structure of the data. Useful as a stopping condition in hierarchical clustering methods (the level with the maximum value for the index captures true structure of the data). For more details check:\n\"AN EXAMINATION OF PROCEDURES FOR DETERMINING THE NUMBER OF CLUSTERS IN A DATA SET\" - Glenn W. Milligan et al.","Gheorghita Catalin Bordianu"]],"ref":"refs/heads/master"}