Generating Concept Hierarchies/Networks: Mining Additional Semantics in Relational Data

Authors:
T. Y. Lin
Affiliations:
-
Venue:
PAKDD '01 Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining
Year:
2001

Citing 5
Cited 1

Variable precision rough set model

Journal of Computer and System Sciences
Rough Sets: Theoretical Aspects of Reasoning about Data

Rough Sets: Theoretical Aspects of Reasoning about Data
Data Mining and Machine Oriented Modeling: A Granular Computing Approach

Applied Intelligence
Finding Association Rules Using Fast Bit Computation: Machine-Oriented Modeling

ISMIS '00 Proceedings of the 12th International Symposium on Foundations of Intelligent Systems
Theory of Relational Databases

Theory of Relational Databases

Table representations of granulations revisited

RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

In relational theory, attribute domains are classical sets; no interactions among attribute values are modeled. So the concept hierarchies, which are additional semantics, used in data mining have to be input by users. In this paper, "real world" data model - relational model with additional semantics specified by binary relational structures (adopt from first order logic) - are explored; in such model, concept hierarchies/networks can be generated automatically. In fact, there are two families of concepts. One family forms a traditional hierarchy. Another forms a hierarchy syntactically, but semantically the hierarchy is a network; this is due to the fact that distinct concepts may be semantically related. A simple example is illustrated.