  Deciding on the unimodality of a dataset is an important problem in data
analysis and statistical modeling. It allows to obtain knowledge about the
structure of the dataset, ie. whether data points have been generated by a
probability distribution with a single or more than one peaks. Such knowledge
is very useful for several data analysis problems, such as for deciding on the
number of clusters and determining unimodal projections. We propose a technique
called UU-test (Unimodal Uniform test) to decide on the unimodality of a
one-dimensional dataset. The method operates on the empirical cumulative
density function (ecdf) of the dataset. It attempts to build a piecewise linear
approximation of the ecdf that is unimodal and models the data sufficiently in
the sense that the data corresponding to each linear segment follows the
uniform distribution. A unique feature of this approach is that in the case of
unimodality, it also provides a statistical model of the data in the form of a
Uniform Mixture Model. We present experimental results in order to assess the
ability of the method to decide on unimodality and perform comparisons with the
well-known dip-test approach. In addition, in the case of unimodal datasets we
evaluate the Uniform Mixture Models provided by the proposed method using the
test set log-likelihood and the two-sample Kolmogorov-Smirnov (KS) test.
