Node Connectivity
There are 3042 nodes that can be used as successor
for a node with an output port of type 
Table.
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 […]
FilteredAssociator (3.6) (legacy)
Class for running an arbitrary associator on data that has been passed through an arbitrary filter. Like the associator, the structure of the filter is […]
GeneralizedSequentialPatterns (3.6) (legacy)
Class implementing a GSP algorithm for discovering sequential patterns in a sequential data set. The attribute identifying the distinct data sequences […]
PredictiveApriori (3.6) (legacy)
Class implementing the predictive apriori algorithm to mine association rules. It searches with an increasing support threshold for the best 'n' rules […]
Tertius (3.6) (legacy)
Finds rules according to confirmation measure (Tertius-type algorithm). For more information see: P. A. Flach, N. Lachiche (1999). Confirmation-Guided […]