Action-Rules: How to Increase Profit of a Company
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
An encyclopaedia of cubature formulas
Journal of Complexity
Applied Intelligence
Introduction to Bayesian Statistics
Introduction to Bayesian Statistics
The GUHA method and its meaning for data mining
Journal of Computer and System Sciences
Action Rules Mining
Observational Calculi and Association Rules
Observational Calculi and Association Rules
Bayesian Statistics: An Introduction
Bayesian Statistics: An Introduction
Hi-index | 0.00 |
The LISp-Miner system for data mining and knowledge discovery uses the GUHA method to comb through a large data base and finds 2 脳 2 contingency tables that satisfy a certain condition given by generalised quantifiers and thereby suggest the existence of possible relations between attributes. In this paper, we show how a more detailed interpretation of the data in the tables that were found by GUHA can be obtained using Bayesian statistical methods. Using a multinomial sampling model and Dirichlet prior, we derive posterior distributions for parameters that correspond to GUHA generalised quantifiers. Examples are presented illustrating the new Bayesian post-processing tools implemented in LISp-Miner. A statistical model for the analysis of contingency tables for data from two subpopulations is also presented.