Cobweb (clustering)

COBWEB is an incremental system for hierarchical conceptual clustering. COBWEB was invented by Professor Douglas H. Fisher, currently at Vanderbilt University.{{cite journal|last=Fisher|first=Douglas|title=Knowledge acquisition via incremental conceptual clustering|journal=Machine Learning|year=1987|volume=2|issue=2|pages=139–172|doi=10.1007/BF00114265|doi-access=free}}{{cite conference|author = Fisher, Douglas H.|title = Improving inference through conceptual clustering|book-title = Proceedings of the 1987 AAAI Conferences|conference = AAAI Conference|location = Seattle Washington|pages = 461–465|date = July 1987}}

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.{{cite book|title=Formal approaches in categorization|publisher=Cambridge University Press|location=Cambridge|isbn=9780521190480|pages=253–273|author=Wayne Iba and Pat Langley|editor=Emmanuel M. Pothos and Andy J. Wills|chapter=Cobweb models of categorization and probabilistic concept formation}}

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:

• Merging Two Nodes
  Merging two nodes means replacing them by a node whose children is the union of the original nodes' sets of children and which summarizes the attribute-value distributions of all objects classified under them.
• Splitting a node
  A node is split by replacing it with its children.
• Inserting a new node
  A node is created corresponding to the object being inserted into the tree.
• Passing an object down the hierarchy
  Effectively calling the COBWEB algorithm on the object and the subtree rooted in the node.