AIM in Unsupervised Data Mining

Publication Type Book Section
Authors Luis Ignacio Lopera Gonzalez, Adrian Derungs, Oliver Amft
Title AIM in Unsupervised Data Mining
Abstract This chapter explores the differences between association rules extracted using the likelihood mining criterion (LMC) and rules extracted by using frequent item-set rule mining (FRM). LMC provides a change in perspective for rule selection, from a measure of frequency in the dataset to a measure of relationship between the rule items. For illustration, this chapter presents the evaluation of qualitative differences between LMC and FRM rules with three examples: (1) a basic rule mining scenario to illustrate LMC properties, (2) an analysis relating socioeconomic information and chemical exposure data, and (3) mining behavior routines in patients undergoing neurological rehabilitation. Results show that LMC is capable of extracting rare rules and does not suffer from support dilution. Furthermore, LMC focuses on the individual event generating processes, while FRM focuses on their commonalities.
Book Title Artificial Intelligence in Medicine
Edition Living reference work
Place Cham
Publisher Springer International Publishing
Date 2021
Pages 1-15
Language en
ISBN 978-3-030-58080-3
URL Publisher's website
Library Catalog Springer Link
Extra DOI: 10.1007/978-3-030-58080-3_300-1
Full Text PDF
Friedrich-Alexander-Universität Erlangen-Nürnberg