INCRAIN: an incremental approach for the gravitational clustering

Authors:
Jonatan Gomez;Juan Peña-Kaltekis;Nestor Romero-Leon;Elizabeth Leon
Affiliations:
Universidad Nacional de Colombia, Computer Systems Engineering Department;Universidad Nacional de Colombia, Computer Systems Engineering Department;Universidad Nacional de Colombia, Computer Systems Engineering Department;Universidad Nacional de Colombia, Computer Systems Engineering Department
Venue:
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
Year:
2007

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Abstract

This paper introduces an incremental data clustering algorithm based on the gravitational law. Basically, data samples are considered as unit-mass particles exposed to gravitational forces. Data points are clustered according their proximity during the simulation of the dynamical system defined by their gravitational fields. When the simulation is stopped, a set of prototypes is generated (several prototypes per cluster found). Each prototype will have associated a mass that is proportional to the number of particles in the sub-cluster and will be used as additional particle when new data samples are given for clustering. Experiments are performed on synthetic data sets and the obtained results are presented.