NDLI: Attribute Generation Based on Association Rules

Content Provider	Springer Nature Link
Author	Terabe, Masahiro Washio, Takashi Motoda, Hiroshi Katai, Osamu Sawaragi, Tetsuo
Copyright Year	2002
Abstract	A decision tree is considered to be appropriate (1) if the tree can classify the unseen data accurately, and (2) 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.
Starting Page	329
Ending Page	349
Page Count	21
File Format	PDF
ISSN	02191377
Journal	Knowledge and Information Systems
Volume Number	4
Issue Number	3
Language	English
Publisher	Springer-Verlag London Limited
Publisher Date	2002-07-11
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Information Systems Human-Computer Interaction Hardware and Architecture Software

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