The problem of classification is one of the major problems associated with data mining. Numerous classification algorithms have been implemented, although there was hardly an algorithm that surpasses all individual algorithms with respect to the standard. A decision tree is a type of classification method in which the end result is the class to which the data belongs. There are many problems faced by decision tree and this paper considers two out of them. First problem faced by decision tree is finding the optimal solution, which is resolved by heuristic techniques more quickly and further efficiently than conventional techniques. Another problem facing a decision tree is scalability issue, which is solved by RainForest framework. RainForest framework considers the scalability problem and has different types of algorithms that work in different types of cases. This article provides a brief overview of the framework of RainForest and Heuristics and steepest ascent hill climbing which are utilized to overcome the scalability issues and the limitation of finding the optimal solution respectively.

Decision tree, Classification, RainForest, Heuristics and Steepest ascent hill climbing

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