Konzeption und Implementierung eines ... - Stephan, Daniel

91 7.5. CLUSTER BENENNUNG UND MATRIZEN
cluster::AssignByThreshold
+ AssignByThreshold()
+ AssignByThreshold(double)
+ assign(double) : ArrayList
+ maxIndex(double) : int
cluster::ClusterStrategy
+ ClusterStrategy()
+ findClusterProbabilityDistribution(Reader) : ClusteringResult
cluster::EMClusterStrategy
cluster::ClusterAssignmentStrategy
+ ClusterAssignmentStrategy()
+ assign(double) : ArrayList
+ toArray(ArrayList) : int[]
InitializingBean
+ findClusterProbabilityDistribution(Reader) : ClusteringResult
+ probabilityDistributionForInstance(Instance) : double[]
+ numberOfInstances() : int
+ instanceAtIndex(int) : Instance
+ afterPropertiesSet() : void
-wekaInstances
analysis::
Instances
+ Instances()
+m_ClusterStrategy
+m_ClusterAssignmentStrategy
cluster::ClusterContext
+ DEFAULT_ARFF_FILE_NAME: String = "clusterInput.arff"
+ m_ClusterStrategy: ClusterStrategy
+ m_ClusterAssignmentStrategy: ClusterAssignmentStrategy
+ ClusterContext()
+ ClusterContext(ClusterStrategy, ClusterAssignmentStrategy)
+ cluster(Matrix) : ClusteringResult
+ assign(ClusteringResult, int) : int
+ assign(double) : ArrayList
«interface»
cluster::ClusteringResult
+ probabilityDistributionForInstance(Instance) : double[]
+ numberOfInstances() : int
+ instanceAtIndex(int) : Instance
cluster::KMeansStrategy
+ KMeansStrategy()
+ findClusterProbabilityDistribution(Reader) : ClusteringResult
+ probabilityDistributionForInstance(Instance) : double[]
+ numberOfInstances() : int
+ instanceAtIndex(int) : Instance
Abbildung 7.5.: Klassendiagramm Cluster Algorithmen