  Broadband noise in gravitational wave (GW) detectors, also known as triggers,
can often be a deterrant to the efficiency with which astrophysical search
pipelines detect sources. It is important to understand their instrumental or
environmental origin so that they could be eliminated or accounted for in the
data. Since the number of triggers is large, data mining approaches such as
clustering and classification are useful tools for this task. Classification of
triggers based on a handful of discrete properties has been done in the past. A
rich information content is available in the waveform or 'shape' of the
triggers that has had a rather restricted exploration so far. This paper
presents a new way to classify triggers deriving information from both trigger
waveforms as well as their discrete physical properties using a sequential
combination of the Longest Common Sub-Sequence (LCSS) and LCSS coupled with
Fast Time Series Evaluation (FTSE) for waveform classification and the
multidimensional hierarchical classification (MHC) analysis for the grouping
based on physical properties. A generalized k-means algorithm is used with the
LCSS (and LCSS+FTSE) for clustering the triggers using a validity measure to
determine the correct number of clusters in absence of any prior knowledge. The
results have been demonstrated by simulations and by application to a segment
of real LIGO data from the sixth science run.
