# Real-Time Anomaly Detection with Subspace Periodic Clustering Approach
## 6 Experimental Analysis and Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**Algorithms**} & \multicolumn{4}{c}{**Evaluation Metrics**} & \multicolumn{2}{c}{**Execution Time**} & \multicolumn{2}{c}{**Periodic Clusters**} \\ \cline{3-6} \multicolumn{1}{c}{} & **Recall** & **Precision** & **F1-Score** & **(in Seconds)** & **Obtained** \\ \hline
1 & \(k\)-means & 0.9605 & 0.9400 & 0.9500 & 28 & \(\times\) \\ \hline
2 & IF model & 0.8301 & 0.850 & 0.8400 & 19 & \(\times\) \\ \hline
3 & SC & 0.6220 & 0.6004 & 0.6110 & 44 & \(\times\) \\ \hline
4 & HDBSCAN & 0.2530 & 0.2300 & 0.2410 & 95 & \(\times\) \\ \hline
5 & ACA & 0.8400 & 0.8010 & 0.8200 & 16 & \(\times\) \\ \hline
6 & LOF & 0.9550 & 0.9390 & 0.9470 & 14 & \(\times\) \\ \hline
7 & SWLOFCC & 0.9665 & 0.9460 & 0.9560 & 12 & \(\times\) \\ \hline
8 & PCM & 0.8800 & 0.8420 & 0.8600 & 26 & \(\times\) \\ \hline
9 & OnCAD & 0.9751 & 0.9650 & 0.9700 & 30 & \(\times\) \\ \hline
10 & MICA & 0.9822 & 0.9780 & 0.9800 & 28 & \(\times\) \\ \hline
11 & Proposed Approach (RADSPCA) & 0.9812 & 0.9790 & 0.9800 & 58 & \(\surd\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparative performances analysis of RADSPCA with some well-known existing methods using KDDCUP’99 [64] dataset. The following observations can be drawn from the obtained results. The \(k\)-means algorithm is quite good as per as the values of the evaluation metrics are concerned. However, it is sensitive to both the dataset and the dimensions. It is also sensitive to the distribution of the dataset in the plane. It cannot supply periodic clusters. The IF model is reasonably good; however, it is efficient up to a certain dimensional dataset, beyond which its efficacy decreases rapidly. It cannot supply periodic clusters. The SC and HDBSCAN algorithms are poor in both performance and execution times. Though. HDBSCAN works very well with lower-dimensional data, but its performance decreases proportionately with the increase in the dimension of the dataset. Both algorithms are not capable of finding periodic clusters. The ACA is reasonably good as far as performance and execution time are concerned. However, it is very sensitive to the order of input to the algorithm. It is not useful for determining periodic clusters. Though the LOF algorithm performs well, it has similar issues as the \(k\)-means algorithm. The SSWLOFCC performs better, and its performance does not depend much on the size of the dataset. However, its execution time increases with the increment of the dimension as well as the size of the dataset. It cannot extract periodic clusters. The PCM is an algorithm consisting of both k-means and hierarchical agglomerative approaches. However, its performance decreases with the increase in size and dimension of the dataset. It cannot be used for finding periodic clusters. OnCAD has a problem with dimensionality. Its accuracy and execution time fall rapidly with the increase in data size and dimensions. It cannot find periodic clusters. The MICA is very good as far as accuracy is concerned. It has recalls of 0.9822 and 0.9832, precisions of 0.978 and 0.977, and F1-scores of 98% and 98% with the KDDCUP'99 [65] and the Kitsune [64] datasets, respectively, which is quite impressive. Its execution time is also quite good. However, it cannot be used for finding periodic clusters. The proposed algorithm (RADSPCA) is better as far as performance is concerned. It has recall, precision, and F1-score values that are almost the same for both datasets. It has recall 0.9812, 0.9860, precision 0.979, 0.9801, and F1-score 0.98, 0.983, with the datasets KDDCUP'99 [64] and Kitsune [65], respectively. It has the ability to extract periodic clusters, which others do not. Though its execution time is a little longer than that of others, the rate of increase is quite low. The extra is the time spent finding subspace and extracting