TY - GEN
T1 - Mining frequent time interval-based event with duration patterns from temporal database
AU - Chen, Kuan Ying
AU - Jaysawal, Bijay Prasad
AU - Huang, Jen Wei
AU - Wu, Yong Bin
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/3/10
Y1 - 2014/3/10
N2 - Time interval-based pattern mining is proposed to improve the lack of the information of time intervals by sequential pattern mining. Previous works of time interval-based pattern mining focused on the relations between events without considering the duration of each event. However, the same event with different time durations will cause definitely different results. For example, if some people cough for one week, they may get a cold for a while. In contrast, if some patients cough for one year, they may get pneumonia in the future. In this work, we propose two algorithms, SARA and SARS, to extract the frequent Time Interval-based Event with Duration, TIED, patterns. TIED patterns not only keep the relations between two events but also reveal the time periods when each event happens and ends. In the experiments, we propose a naive algorithm and modify a previous algorithm to compare the performances with SARA and SARS. The experimental results show that SARA and SARS are more efficient in execution time and memory usage than other two algorithms.
AB - Time interval-based pattern mining is proposed to improve the lack of the information of time intervals by sequential pattern mining. Previous works of time interval-based pattern mining focused on the relations between events without considering the duration of each event. However, the same event with different time durations will cause definitely different results. For example, if some people cough for one week, they may get a cold for a while. In contrast, if some patients cough for one year, they may get pneumonia in the future. In this work, we propose two algorithms, SARA and SARS, to extract the frequent Time Interval-based Event with Duration, TIED, patterns. TIED patterns not only keep the relations between two events but also reveal the time periods when each event happens and ends. In the experiments, we propose a naive algorithm and modify a previous algorithm to compare the performances with SARA and SARS. The experimental results show that SARA and SARS are more efficient in execution time and memory usage than other two algorithms.
UR - http://www.scopus.com/inward/record.url?scp=84946688444&partnerID=8YFLogxK
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U2 - 10.1109/DSAA.2014.7058125
DO - 10.1109/DSAA.2014.7058125
M3 - Conference contribution
AN - SCOPUS:84946688444
T3 - DSAA 2014 - Proceedings of the 2014 IEEE International Conference on Data Science and Advanced Analytics
SP - 548
EP - 554
BT - DSAA 2014 - Proceedings of the 2014 IEEE International Conference on Data Science and Advanced Analytics
A2 - Karypis, George
A2 - Cao, Longbing
A2 - Wang, Wei
A2 - King, Irwin
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2014
Y2 - 30 October 2014 through 1 November 2014
ER -