Cobweb (clustering)

COBWEB is an incremental system for hierarchical conceptual clustering. COBWEB was invented by Professor Douglas H. Fisher, currently at Vanderbilt University.[1][2]

COBWEB incrementally organizes observations into a classification tree. Each node in a classification tree represents a class (concept) and is labeled by a probabilistic concept that summarizes the attribute-value distributions of objects classified under the node. This classification tree can be used to predict missing attributes or the class of a new object.[3]

There are four basic operations COBWEB employs in building the classification tree. Which operation is selected depends on the category utility of the classification achieved by applying it. The operations are:

The COBWEB Algorithm

  COBWEB(root, record):
  Input: A COBWEB node root, an instance to insert record
  if root has no children then
    children := {copy(root)}
    newcategory(record) \\ adds child with record’s feature values.
    insert(record, root) \\ update root’s statistics
  else
    insert(record, root)
    for child in root’s children do
      calculate Category Utility for insert(record, child),
      set best1, best2 children w. best CU.
    end for
    if newcategory(record) yields best CU then
      newcategory(record)
    else if merge(best1, best2) yields best CU then
      merge(best1, best2)
      COBWEB(root, record)
    else if split(best1) yields best CU then
      split(best1)
      COBWEB(root, record)
    else
      COBWEB(best1, record)
    end if
  end

External links

References

  1. Fisher, Douglas (1987). "Knowledge acquisition via incremental conceptual clustering". Machine Learning 2 (2): 139–172. doi:10.1007/BF00114265.
  2. Fisher, Douglas H. (July 1987). "Improving inference through conceptual clustering". Proceedings of the 1987 AAAI Conferences. AAAI Conference. Seattle Washington. pp. 461–465.
  3. William Iba and Pat Langley. "Cobweb models of categorization and probabilistic concept formation". In Emmanuel M. Pothos and Andy J. Wills,. Formal approaches in categorization. Cambridge: Cambridge University Press. pp. 253–273. ISBN 9780521190480.