The demonstration of SMART II using QPSK dataset in D1 example.

<p>Sub-figures (1) – (8) demonstrate the procedure of SMART II. It starts with (sub-figure(1)), splits into . , and shown sub-figures(2) – (5) respectively, and then merges some clusters while splitting as shown in sub-figures(6) – (8). Sub-figure(9) is the final clustering result. Parameter setting: .</p

Performance comparison of SMART I and II with variable values of .

<p>Performance comparison of SMART I and II with variable values of .</p

Performance comparison of many metrics, including , MML, CH, SI for all algorithms in yeast cell cycle dataset.

<p>Performance comparison of many metrics, including , MML, CH, SI for all algorithms in yeast cell cycle dataset.</p

The demonstration of SMART II using Gaussian mixture dataset in D2 example.

<p>Sub-figures (1) – (8) demonstrate the procedure of SMART II. SMART II starts from as shown in sub-figures(1) and (2), splits the dataset to and shown in sub-figures(3) and (4); the merging commences while splitting continues as shown in sub-figures(5) – (8). Sub-figure(9) is the final clustering result. Parameter setting: .</p

Performance comparison of many metrics, including CSR, , MML, CH, SI for all algorithms in Leukemia dataset.

<p>Performance comparison of many metrics, including CSR, , MML, CH, SI for all algorithms in Leukemia dataset.</p

The list of Software with which all clustering methods in this paper are implemented.

<p>The list of Software with which all clustering methods in this paper are implemented.</p

The pseudo-code for SMART II.

<p>The pseudo-code for SMART II.</p

The pseudo-code for SMART I.

<p>The pseudo-code for SMART I.</p

Comparison of running time (seconds) of the algorithms implemented in MATLAB (upper section) and other platforms (lower section) for two real datasets respectively.

<p>Comparison of running time (seconds) of the algorithms implemented in MATLAB (upper section) and other platforms (lower section) for two real datasets respectively.</p

The errorbar charts of (a) ARI, (b) JI, (c) CSR, and (d) NMI for all compared algorithms in S2 datasets.

<p>The vertical axis in each sub-figure represents individual index and the horizontal axis is parameter pairs from PP1 to PP13, representing 13 noise levels from low to high. For SMART I, ; for SMART I and II, . For ULFMM, SSMCL, and VBGM, .</p