Mining generalized knowledge from ordered data through attribute-oriented induction techniques

Abstract The attribute-oriented induction (AOI for short) method is one of the most important data mining methods. The input of the AOI method contains a relational table and a concept tree (concept hierarchy) for each attribute, and the output is a small relation summarizing the general characteristics of the task-relevant data. Although AOI is very useful for inducing general characteristics, it has the limitation that it can only be applied to relational data, where there is no order among the data items. If the data are ordered, the existing AOI methods are unable to find the generalized knowledge. In view of this weakness, this paper proposes a dynamic programming algorithm, based on AOI techniques, to find generalized knowledge from an ordered list of data. By using the algorithm, we can discover a sequence of K generalized tuples describing the general characteristics of different segments of data along the list, where K is a parameter specified by users.

[1]  Sally McClean,et al.  Incorporating domain knowledge into attribute‐oriented data mining , 2000 .

[2]  Jiawei Han,et al.  Generalization-Based Data Mining in Object-Oriented Databases Using an Object Cube Model , 1998, Data Knowl. Eng..

[3]  Jiawei Han,et al.  Efficient Rule-Based Attribute-Oriented Induction for Data Mining , 2000, Journal of Intelligent Information Systems.

[4]  Jiawei Han,et al.  Knowledge Discovery in Databases: An Attribute-Oriented Approach , 1992, VLDB.

[5]  Jiawei Han,et al.  An attribute-oriented approach for learning classification rules from relational databases , 1990, [1990] Proceedings. Sixth International Conference on Data Engineering.

[6]  Petra Perner,et al.  Data Mining - Concepts and Techniques , 2002, Künstliche Intell..

[7]  Philip S. Yu,et al.  Data Mining: An Overview from a Database Perspective , 1996, IEEE Trans. Knowl. Data Eng..

[8]  Shusaku Tsumoto,et al.  Knowledge discovery in clinical databases and evaluation of discovered knowledge in outpatient clinic , 2000, Inf. Sci..

[9]  Xiaohua Hu,et al.  Mining knowledge rules from databases: a rough set approach , 1996, Proceedings of the Twelfth International Conference on Data Engineering.

[10]  Howard J. Hamilton,et al.  Efficient Attribute-Oriented Generalization for Knowledge Discovery from Large Databases , 1998, IEEE Trans. Knowl. Data Eng..

[11]  Beng Chin Ooi,et al.  Discovery of General Knowledge in Large Spatial Databases , 1993 .

[12]  Nick Cercone,et al.  GRG: knowledge discovery using information generalization, information reduction, and rule generation , 1995, Proceedings of 7th IEEE International Conference on Tools with Artificial Intelligence.

[13]  Howard J. Hamilton,et al.  Performance evaluation of attribute-oriented algorithms for knowledge discovery from databases , 1995, Proceedings of 7th IEEE International Conference on Tools with Artificial Intelligence.

[14]  Jiawei Han,et al.  Data-Driven Discovery of Quantitative Rules in Relational Databases , 1993, IEEE Trans. Knowl. Data Eng..