Node Connectivity
There are 4972 nodes that can be used as predessesor
for a node with an input port of type 
Generic Port.
OneR (3.6) (legacy)
Class for building and using a 1R classifier; in other words, uses the minimum-error attribute for prediction, discretizing numeric attributes. For more […]
PART (3.6) (legacy)
Class for generating a PART decision list. Uses separate-and-conquer. Builds a partial C4.5 decision tree in each iteration and makes the "best" leaf into a […]
Prism (3.6) (legacy)
Class for building and using a PRISM rule set for classification. Can only deal with nominal attributes. Can't deal with missing values. Doesn't do any […]
Ridor (3.6) (legacy)
An implementation of a RIpple-DOwn Rule learner. It generates a default rule first and then the exceptions for the default rule with the least (weighted) […]
ZeroR (3.6) (legacy)
Class for building and using a 0-R classifier. Predicts the mean (for a numeric class) or the mode (for a nominal class).
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. […]