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

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

0.00 Avg rating0 Votes
Article ID: iaor20061883
Country: Netherlands
Volume: 166
Issue: 1
Start Page Number: 221
End Page Number: 245
Publication Date: Oct 2005
Journal: European Journal of Operational Research
Authors: ,
Keywords: programming: dynamic, datamining
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.

Reviews

Required fields are marked *. Your email address will not be published.