Attribute-oriented induction using domain generalization graphs

• Main Authors: Hamilton, H.J., Hilderman, R.J., Cercone, N.
• Format: Conference Proceeding
• Language: English
• Subjects: Classification tree analysis; Computer science; Data analysis; Decision trees; Entropy; Frequency; Global Positioning System; Marketing and sales; Partitioning algorithms; Relational databases
• ISSN: 1082-3409; 2375-0197
• DOI: 10.1109/TAI.1996.560458

Attribute-oriented induction summarizes the information in a relational database by repeatedly replacing specific attribute values with more general concepts according to user-defined concept hierarchies. We show how domain generalization graphs can be constructed from multiple concept hierarchies associated with an attribute, describe how these graphs can be used to control the generalization of a set of attributes, and present the Multi-Attribute Generalization algorithm for attribute-oriented induction using domain generalization graphs. Based upon a generate-and-test approach, the algorithm generates all possible combinations of nodes from the domain generalization graphs associated with the individual attributes, to produce all possible generalized relations for the set of attributes. We rant the interestingness of the resulting generalized relations using measures based upon relative entropy and variance. Our experiments show that these measures provide a basis for analyzing summary data from relational databases. Variance appears more useful because it tends to rank the less complex generalized relations (i.e., those with few attributes and/or few tuples) as more interesting.