Efficient unimodality test in clustering by signature testing
This work addresses a specific bottleneck in hierarchical clustering for data analysts, offering an incremental improvement over existing unimodality tests.
The paper tackles the problem of detecting unimodality in clustering by introducing a new test called Sigtest, which transforms data to reduce variation and improves accuracy in identifying overlapped clusters while significantly lowering computational complexity.
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.