  In this work we seek clusters of genomic words in human DNA by studying their
inter-word lag distributions. Due to the particularly spiked nature of these
histograms, a clustering procedure is proposed that first decomposes each
distribution into a baseline and a peak distribution. An outlier-robust fitting
method is used to estimate the baseline distribution (the `trend'), and a
sparse vector of detrended data captures the peak structure. A simulation study
demonstrates the effectiveness of the clustering procedure in grouping
distributions with similar peak behavior and/or baseline features. The
procedure is applied to investigate similarities between the distribution
patterns of genomic words of lengths 3 and 5 in the human genome. These
experiments demonstrate the potential of the new method for identifying words
with similar distance patterns.
