Authors:
Salatiel Ezennaya-Gomez
1
and
Christian Borgelt
2
Affiliations:
1
European Centre for Soft Computing and Otto-von-Guericke University, Spain
;
2
European Center for Soft Computing, Spain
Keyword(s):
Graded Synchrony, Synchronous Events, Temporal Imprecision, Selective Participation, Frequent Pattern Mining.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Data Manipulation
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Soft Computing
Abstract:
We consider the task of finding frequent parallel episodes in parallel point processes (or event sequences),
allowing for imprecise synchrony of the events constituting occurrences (temporal imprecision) as well as
incomplete occurrences (selective participation). The temporal imprecision problem is tackled by frequent
pattern mining using a graded notion of synchrony that captures both the number of instances of a pattern as
well as the precision of synchrony of its events. To cope with selective participation, a reduction sequence of
items (or event types) is formed based on found frequent patterns and guided by pattern overlap. We evaluate
the performance of this method on a large number of data sets with injected parallel episodes. We demonstrate
that, in contrast to binary synchrony where it pays to consider the pattern instances, graded synchrony performs
better with a pattern-based scheme than with an instance-based one, thus simplifying the procedure.