Stephen Shaoyi Liao | PubMed Health

PubMed2006

A functional-dependencies-based Bayesian networks learning method and its application in a mobile commerce system.

This paper presents a new method for learning Bayesian networks from functional dependencies (FD) and third normal form (3NF) tables in relational databases. The method sets up a linkage between the theory of relational databases and probabilistic reasoning models, which is interesting and useful especially when data are incomplete and inaccurate. The effectiveness and practicability of the proposed method is demonstrated by its implementation in a mobile commerce system.

Algorithms↗

PubMed2004

Finding relevant sequences in time series containing crisp, interval, and fuzzy interval data.

Finding similar sequences in time series has received much attention and is a widely studied topic. Most existing approaches in the time series area focus on the efficiency of algorithms but seldom provide a means to handle imprecise data. In this paper, a more general approach is proposed to measure the distance of time sequences containing crisp values, intervals, and fuzzy intervals as well. The concept of distance measurement and its associated dynamic-programming-based algorithms are described. In addition to finding the sequences with similar evolving trends, a means of finding the sequences with opposite evolving tendencies is also proposed, which is usually omitted in current related research but could be of great interest to many users.

Algorithms↗