@INPROCEEDINGS { Rsds3285,
author = {Freeman, Rachel L. and Busse-Grzymala, Jerzy W. and Riffel, Laura A. and Schroeder, Stephen R.}, 
title = {A Self-Injurious Behavior Data Set Analyzed by Data Mining System LERS}, 
booktitle = {Proceedings of International Workshop on Rough Set Theory and Granular Computing (RSTGC-2001)}, 
conference = {International Workshop on Rough Set Theory and Granular Computing}, 
series =  = {Bulletin of International Rough Set Society}, 
volume = 5, 
pages = {195-200}, 
month = {May}, 
year = 2001, 
editor = {Hirano, Shoji and Inuiguchi, Masahiro and Tsumoto, Shusaku}, 
issn = {1346-0013}, 
abstract = {The data mining system LERS (Learning from Examples based on Rough Sets) was to explore the relation between arousal level, environmental events and the occurrence of problem behavior. Data from this study were collected using an athlete's heart rate monitor to gather heart rate every 15 seconds while the subject was videotaped in his home environment. We used data showing recorded behaviors and environmental events in real times. Thus our input data were temporal. The input data were converted into a transformed data set, describing the subject's behavior in terms of current external stimuli and heart rate as well as values of both external stimuli and heart rate from the previous 15 seconds and 30 second intervals. The transformed data set was consistent, hence LERS induced a certain rule set and a possible rule set. These rules show interesting relations between subjects behavior, external stimuli and heart rate.}, 
keywords = {self-injurious behavior, data mining, knowledge discovery, temporal data, system LERS, }, 
}