Node Connectivity
There are 12 nodes that can be used as predessesor
for a node with an input port of type 
Weka 3.6 Cluster.
CLOPE (3.6) (legacy)
Yiling Yang, Xudong Guan, Jinyuan You: CLOPE: a fast and effective clustering algorithm for transactional data. In: Proceedings of the eighth ACM SIGKDD […]
Cobweb (3.6) (legacy)
Class implementing the Cobweb and Classit clustering algorithms. Note: the application of node operators (merging, splitting etc.) in terms of ordering and […]
DBScan (3.6) (legacy)
Martin Ester, Hans-Peter Kriegel, Joerg Sander, Xiaowei Xu: A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. In: […]
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 […]