By Topic

Sequential Data Mining: A Comparative Case Study in Development of Atherosclerosis Risk Factors

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

5 Author(s)
Jira Klema ; Czech Tech. Univ. in Prague, Prague ; Lenka Novakova ; Filip Karel ; Olga Stepankova
more authors

Sequential data represent an important source of potentially new medical knowledge. However, this type of data is rarely provided in a format suitable for immediate application of conventional mining algorithms. This paper summarizes and compares three different sequential mining approaches based, respectively, on windowing, episode rules, and inductive logic programming. Windowing is one of the essential methods of data preprocessing. Episode rules represent general sequential mining, while inductive logic programming extracts first-order features whose structure is determined by background knowledge. The three approaches are demonstrated and evaluated in terms of a case study STULONG. It is a longitudinal preventive study of atherosclerosis where the data consist of a series of long-term observations recording the development of risk factors and associated conditions. The intention is to identify frequent sequential/temporal patterns. Possible relations between the patterns and an onset of any of the observed cardiovascular diseases are also studied.

Published in:

IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews)  (Volume:38 ,  Issue: 1 )