A study on the modified attribute oriented induction algorithm of mining the multi-value attribute data

Authors:
Shu-Meng Huang;Ping-Yu Hsu;Wan-Chih Wang
Affiliations:
Department of Business Administration, National Central University, Jhongli City, Taoyuan County, Taiwan (R.O.C) and Department of Marketing and Distribution Management, Hsing Wu Institute of Tech ...;Department of Business Administration, National Central University, Jhongli City, Taoyuan County, Taiwan (R.O.C);Graduate Institute of Management Sciences, Tamkang University, New Taipei City, Taiwan (R.O.C)
Venue:
ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part I
Year:
2012

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Abstract

Attribute Oriented Induction method (short for AOI) is one of the most important methods of data mining. The input value of AOI contains a relational data table and attribute-related concept hierarchies. The output is a general feature inducted by the related data. Though it is useful in searching for general feature with traditional AOI method, it only can mine the feature from the single-value attribute data. If the data is of multiple-value attribute, the traditional AOI method is not able to find general knowledge from the data. In addition, the AOI algorithm is based on the way of induction to establish the concept hierarchies. Different principles of classification or different category values produce different concept trees, therefore, affecting the inductive conclusion. Based on the issue, this paper proposes a modified AOI algorithm combined with a simplified Boolean bit Karnaugh map. It does not need to establish the concept tree. It can handle data of multi value and find out the general features implied within the attributes.