document: Attribute generation based on association rules A decision tree is considered to be appropriate -LRB- 1 -RRB- if the tree can classify the unseen data accurately , and -LRB- 2 -RRB- if the size of the tree is small . One of the approaches to induce such a good decision tree is to add new attributes and their values to enhance the expressiveness of the training data at the data pre-processing stage . There are many existing methods for attribute extraction and construction , but constructing new attributes is still an art . These methods are very time consuming , and some of them need a priori knowledge of the data domain . They are not suitable for data mining dealing with large volumes of data . We propose a novel approach that the knowledge on attributes relevant to the class is extracted as association rules from the training data . The new attributes and the values are generated from the association rules among the originally given attributes . We elaborate on the method and investigate its feature . The effectiveness of our approach is demonstrated through some experiments
doc_bio_tags: B I O O B I O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O B I O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O B I O O B I O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O B
extractive_keyphrases: attribute generation association rules decision tree training data attribute extraction data mining experiments
abstractive_keyphrases: large database