UniForCE: The Unimodality Forest Method for Clustering and Estimation of
  the Number of Clusters
Georgios Vardakas Argyris Kalogeratos Aristidis Likas
Estimating the number of clusters k while clustering the data is a
challenging task. An incorrect cluster assumption indicates that the number of
clusters k gets wrongly estimated. Consequently, the model fitting becomes less
important. In this work, we focus on the concept of unimodality and propose a
flexible cluster definition called locally unimodal cluster. A locally unimodal
cluster extends for as long as unimodality is locally preserved across pairs of
subclusters of the data. Then, we propose the UniForCE method for locally
unimodal clustering. The method starts with an initial overclustering of the
data and relies on the unimodality graph that connects subclusters forming
unimodal pairs. Such pairs are identified using an appropriate statistical
test. UniForCE identifies maximal locally unimodal clusters by computing a
spanning forest in the unimodality graph. Experimental results on both real and
synthetic datasets illustrate that the proposed methodology is particularly
flexible and robust in discovering regular and highly complex cluster shapes.
Most importantly, it automatically provides an adequate estimation of the
number of clusters.