Discovery of Arbitrary-Shapes Clusters Using
DENCLUE Algorithm
Mariam Khader1
and Ghazi Al-Naymat2,1
1Departmentof Computer Science, Princess Sumaya
University for Technology, Jordan
2Department
of IT, Ajman University, UAE
Abstract: One of the main requirements in clustering spatial
datasets is the discovery of clusters with arbitrary-shapes. Density-based
algorithms satisfy this requirement by forming clusters as dense regions in the
space that are separated by sparser regions. DENCLUE is a density-based
algorithm that generates a compact mathematical form of arbitrary-shapes
clusters. Although DENCLUE has proved its efficiency, it cannot handle large
datasets since it requires large computation complexity. Several attempts were
proposed to improve the performance of DENCLUE algorithm, including DENCLUE 2.
In this study, an empirical evaluation is conducted to highlight the
differences between the first DENCLUE variant which uses the Hill-Climbing
search method and DENCLUE 2 variant, which uses the fast Hill-Climbing method. The
study aims to provide a base for further enhancements on both algorithms. The
evaluation results indicate that DENCLUE 2 is faster than DENCLUE 1. However,
the first DECNLUE variant outperforms the second variant in discovering
arbitrary-shapes clusters.
Keywords: Clustering, DENCLUE, Density Clustering, Hill-Climbing.
Received January 24, 2020;
accepted June 9, 2020