Class implementing the Cobweb and Classit clustering algorithms.
Note: the application of node operators (merging, splitting etc.) in terms of ordering and priority differs (and is somewhat ambiguous) between the original Cobweb and Classit papers. This algorithm always compares the best host, adding a new leaf, merging the two best hosts, and splitting the best host when considering where to place a new instance.
For more information see:
D. Fisher (1987). Knowledge acquisition via incremental conceptual clustering. Machine Learning. 2(2):139-172.
J. H. Gennari, P. Langley, D. Fisher (1990). Models of incremental concept formation. Artificial Intelligence. 40:11-61.
The table below describes the options available for Cobweb.
set the minimum standard deviation for numeric attributes
set the category utility threshold by which to prune nodes
save instance information for visualization purposes
The random number seed to be used.
The table below describes the capabilites of Cobweb.
Missing values, Nominal attributes, Unary attributes, Binary attributes, Empty nominal attributes, Date attributes, Numeric attributes
Min # of instances