2 research outputs found
Efficient unimodality test in clustering by signature testing
This paper provides a new unimodality test with application in hierarchical
clustering methods. The proposed method denoted by signature test (Sigtest),
transforms the data based on its statistics. The transformed data has much
smaller variation compared to the original data and can be evaluated in a
simple proposed unimodality test. Compared with the existing unimodality tests,
Sigtest is more accurate in detecting the overlapped clusters and has a much
less computational complexity. Simulation results demonstrate the efficiency of
this statistic test for both real and synthetic data sets