# Incrementally Mining Column Constant Biclusters with FVSP Tree
## 3 Experiments
### Experimental Results
#### 3.2.7 Testing on the WebDocs Dataset  
In addition to the five small datasets, one big dataset WebDocs [[http://fimi.uantwerpen.be/data/](http://fimi.uantwerpen.be/data/)]([http://fimi.uantwerpen.be/data/](http://fimi.uantwerpen.be/data/)) (accessed on 11 October 2022) is tested. The size of T10I4D100K is 1.4 GB. It contains 1,692,082 samples. Running in a batch way fails because the required memory for the batch method is bigger than the maximal available memory. For the incremental method, WebDocs is divided into 20 equal subsets (71 MB), the required maximal memory is about 1.2 GB. The incremental method succeeds in running.  
The final FVSFP tree and CCBs produced in both \"Incremental\" and \"Batch\" ways are identical. The average saved runtime and memory on five datasets is shown in Table 11. It can be found that the bigger the dataset size is, the longer time and more memory can be saved. If the dataset size is TB-level, the advantage of the proposed method can be more apparent.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Minimal Support Rate**} & \multirow{2}{*}{**Methods**} & **Runtime (s)** & **Memory (MB)** \\  & & **(Mean \(\pm\) std)** & **(Mean \(\pm\) std)** \\ \hline \multirow{2}{*}{0.01} & Incremental & **2.03 \(\pm\) 0.12** & **20.54 \(\pm\) 0.13** \\ \cline{2-4}  & Batch & 3.19 \(\pm\) 0.46 & 26.88 \(\pm\) 0.14 \\ \hline \multirow{2}{*}{0.008} & Incremental & **2.04 \(\pm\) 0.02** & **21.52 \(\pm\) 0.14** \\ \cline{2-4}  & Batch & 3.27 \(\pm\) 0.18 & 26.98 \(\pm\) 0.08 \\ \hline \multirow{2}{*}{0.006} & Incremental & **2.31 \(\pm\) 0.04** & **19.64 \(\pm\) 0.23** \\ \cline{2-4}  & Batch & 3.65 \(\pm\) 0.262 & 27.14 \(\pm\) 0.11 \\ \hline \multirow{2}{*}{0.004} & Incremental & **3.20 \(\pm\) 0.12** & **21.08 \(\pm\) 0.13** \\ \cline{2-4}  & Batch & 4.20 \(\pm\) 0.25 & 27.58 \(\pm\) 0.04 \\ \hline \multirow{2}{*}{0.002} & Incremental & **5.40 \(\pm\) 0.20** & **20.46 \(\pm\) 0.13** \\ \cline{2-4}  & Batch & 7.62 \(\pm\) 0.23 & 28.62 \(\pm\) 0.08 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Experimental results on the BIRADS dataset. (bold means better result)  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Minimal Support Rate**} & \multirow{2}{*}{**Methods**} & **Runtime (s)** & **Memory (MB)** \\  & & **(Mean \(\pm\) std)** & **(Mean \(\pm\) std)** \\ \hline \multirow{2}{*}{0.01} & Incremental & **37.76 \(\pm\) 0.48** & **236.62 \(\pm\) 3.75** \\ \cline{2-4}  & Batch & 67.8 \(\pm\) 1.48 & 367.48 \(\pm\) 0.25 \\ \hline \multirow{2}{*}{0.008} & Incremental & **37.77 \(\pm\) 0.48** & **239.36 \(\pm\) 0.32** \\ \cline{2-4}  & Batch & 67.2 \(\pm\) 0.44 & 367.62 \(\pm\) 0.08 \\ \hline \multirow{2}{*}{0.006} & Incremental & **39.16 \(\pm\) 0.46** & **238.86 \(\pm\) 1.19** \\ \cline{2-4}  & Batch & 68.6 \(\pm\) 2.07 & 369.26 \(\pm\) 2.54 \\ \hline \multirow{2}{*}{0.004} & Incremental & **40.91 \(\pm\) 1.46** & **240.8 \(\pm\) 4.40** \\ \cline{2-4}  & Batch & 70.4 \(\pm\) 2.07 & 368.84 \(\pm\) 0.11 \\ \hline \multirow{2}{*}{0.002} & Incremental & **60.92 \(\pm\) 3.52** & **244.08 \(\pm\) 3.30** \\ \cline{2-4}  & Batch & 90 \(\pm\) 7.84 & 371.38 \(\pm\) 0.04 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Experiment results on semeion dataset. (bold means better result)