Node Connectivity
There are 2898 nodes that can be used as successor
for a node with an output port of type 
Table.
EM (3.6) (legacy)
Simple EM (expectation maximisation) class. EM assigns a probability distribution to each instance which indicates the probability of it belonging to each […]
FarthestFirst (3.6) (legacy)
Cluster data using the FarthestFirst algorithm. For more information see: Hochbaum, Shmoys (1985). A best possible heuristic for the k-center problem. […]
FilteredClusterer (3.6) (legacy)
Class for running an arbitrary clusterer on data that has been passed through an arbitrary filter. Like the clusterer, the structure of the filter is based […]
MakeDensityBasedClusterer (3.6) (legacy)
Class for wrapping a Clusterer to make it return a distribution and density. Fits normal distributions and discrete distributions within each cluster […]
OPTICS (3.6) (legacy)
Mihael Ankerst, Markus M. Breunig, Hans-Peter Kriegel, Joerg Sander: OPTICS: Ordering Points To Identify the Clustering Structure. In: ACM SIGMOD […]
SimpleKMeans (3.6) (legacy)
Cluster data using the k means algorithm. Can use either the Euclidean distance (default) or the Manhattan distance. If the Manhattan distance is used, then […]
XMeans (3.6) (legacy)
Cluster data using the X-means algorithm. X-Means is K-Means extended by an Improve-Structure part In this part of the algorithm the centers are attempted […]
sIB (3.6) (legacy)
Cluster data using the sequential information bottleneck algorithm. Note: only hard clustering scheme is supported. sIB assign for each instance the […]
Apriori (3.6) (legacy)
Class implementing an Apriori-type algorithm. Iteratively reduces the minimum support until it finds the required number of rules with the given minimum […]
FPGrowth (3.6) (legacy)
Class implementing the FP-growth algorithm for finding large item sets without candidate generation. Iteratively reduces the minimum support until it finds […]