  One class of gravitational wave signals LIGO is searching for consists of
short duration bursts of unknown waveforms. Potential sources include core
collapse supernovae, gamma ray burst progenitors, and mergers of binary black
holes or neutron stars. We present a density-based clustering algorithm to
improve the performance of time-frequency searches for such gravitational-wave
bursts when they are extended in time and/or frequency, and not sufficiently
well known to permit matched filtering. We have implemented this algorithm as
an extension to the QPipeline, a gravitational-wave data analysis pipeline for
the detection of bursts, which currently determines the statistical
significance of events based solely on the peak significance observed in
minimum uncertainty regions of the time-frequency plane. Density based
clustering improves the performance of such a search by considering the
aggregate significance of arbitrarily shaped regions in the time-frequency
plane and rejecting the isolated minimum uncertainty features expected from the
background detector noise. In this paper, we present test results for simulated
signals and demonstrate that density based clustering improves the performance
of the QPipeline for signals extended in time and/or frequency.

General Relativity and Quantum Cosmology;Instrumentation and Methods for Astrophysics;Computer Vision and Pattern Recognition;Data Analysis, Statistics and Probability