Search Framework for Mining Classi cation

Classiication{rule{learning task is presented as a search process of nding a classiication{ decision tree that meets users' preferences and requirements. Users can control the eeciency of the mining process and the quality of the nal decision tree through the search parameters. This search framework allows users to easily adapt to diierent domains and diierent sets of data by modifying diierent search parameters. The mining process starts with a speciic set of values for the search parameters and the process can be repeated with diierent search parameters values until a satisfactory result is obtained. This framework also allows the development of new algorithms. A set of search parameters that is frequently and successfully used can deene a new algorithm. We present two new algorithms developed in this search framework and compare them to well{known algorithms. BF uses best{{rst ordering to expand the frontier nodes of the decision trees, instead of the conventional depth{{rst or breadth{{rst criteria. CDP+ dynamically adjusts the depth pruning criteria for a node. Experimental results show that BF has a new capability to guide the construction of decision trees based on the overall error{rate criteria. In addition, CDP+ often outperforms several decision{tree learning algorithms in error rate and number of nodes generated. the content of which does not necessarily reeect the position or the policy of the government, and no oocial endorsement should be inferred.