def k_means_clustering(data, featureWeightMap, show=False): SimilarityCalc = similarityCalculator.SimilarityCalculator(featureWeightMap) attribute_clusters = _compute_k_means_clusters(data, SimilarityCalc.simiarity_according_to_attributes, 5) attribute_and_friendship_clusters = _compute_k_means_clusters(data, SimilarityCalc.simiarity_according_to_attributes_and_friendship, 10) weighted_attribute_and_friendship_clusters = _compute_k_means_clusters(data, SimilarityCalc.similarity_weighted_attributes_friendship, 3.5) if show: visualizer = Visualizer() for personID in data.persons.getOriginalPersons(): visualizer.visualizeClusters( attribute_clusters[personID] ) visualizer.visualizeClusters( attribute_and_friendship_clusters[personID] ) return attribute_clusters, attribute_and_friendship_clusters, weighted_attribute_and_friendship_clusters
def k_means_clustering(data, featureWeightMap, show=False): SimilarityCalc = similarityCalculator.SimilarityCalculator( featureWeightMap) attribute_clusters = _compute_k_means_clusters( data, SimilarityCalc.simiarity_according_to_attributes, 5) attribute_and_friendship_clusters = _compute_k_means_clusters( data, SimilarityCalc.simiarity_according_to_attributes_and_friendship, 10) weighted_attribute_and_friendship_clusters = _compute_k_means_clusters( data, SimilarityCalc.similarity_weighted_attributes_friendship, 3.5) if show: visualizer = Visualizer() for personID in data.persons.getOriginalPersons(): visualizer.visualizeClusters(attribute_clusters[personID]) visualizer.visualizeClusters( attribute_and_friendship_clusters[personID]) return attribute_clusters, attribute_and_friendship_clusters, weighted_attribute_and_friendship_clusters