Decision tree algorithms such as See5 (or C5) are typicallyused in data mining for classi¯cation and prediction purposes. In thisstudy we propose EXPLORE, a novel decision tree algorithm, which is amodi¯cation of See5. The modi¯cations are made to improve the capabilityof a tree in extracting hidden patterns. Justi¯cation of the proposedmodi¯cations is also presented. We experimentally compare EXPLOREwith some existing algorithms such as See5, REPTree and J48 on severalissues including quality of extracted rules/patterns, simplicity, and classi¯cation accuracy of the trees. Our initial experimental results indicateadvantages of EXPLORE over existing algorithms.
Original languageEnglish
Pages (from-to)55-71
Number of pages17
JournalLecture Notes in Computer Science
Publication statusPublished - 2012


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