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
