TY  - CHAP
T1  - Behavioral Clustering for Point Processes
A1  - Braune, Christian
A1  - Borgelt, Christian
A1  - Kruse, Rudolf
ED  - Tucker, Allan
ED  - Höppner, Frank
ED  - Siebes, Arno
ED  - Swift, Steven
TI  - Advances in Intelligent Data Analysis XII
T3  - Lecture Notes in Computer Science
Y1  - 2013
VL  - 8207
SP  - 127
EP  - 137
PB  - Springer Berlin Heidelberg
SN  - 978-3-642-41397-1
UR  - http://dx.doi.org/10.1007/978-3-642-41398-8_12
M2  - doi: 10.1007/978-3-642-41398-8_12
KW  - clustering
KW  - point processes
KW  - spike train analysis
N2  - Groups of (parallel) point processes may be analyzed with a variety of different goals. Here we consider the case in which one has a special interest in finding subgroups of processes showing a behavior that differs significantly from the other processes. In particular, we are interested in finding subgroups that exhibit an increased synchrony. Finding such groups of processes poses a difficult problem as its naïve solution requires enumerating the power set of all processes involved, which is a costly procedure. In this paper we propose a method that allows us to efficiently filter the process set for candidate subgroups. We pay special attention to the possibilities of temporal imprecision, meaning that the synchrony is not exact, and selective participation, meaning that only a subset of the related processes participates in each synchronous event.
ER  -