# Real-Time Anomaly Detection with Subspace Periodic Clustering Approach
## 7 Conclusions, Limitations and Lines for Future Works
### Limitations and Future Directions of Work  
The proposed RADSPCA has some limitations. Firstly, it is unable to deal with continuous data, as a rough set is inefficient to handle continuous data. Secondly, the method uses the k-means algorithm. It has the following issues with finding anomalies: For example, the centroid of any cluster can be pulled by anomalies, or there may be a  
Figure 7: The execution time with respect to the dimension using Kitsune dataset [65].  
cluster of anomalies extracted by the method that looks such as a normal cluster. Finally, the method cannot detect anomalies from temporal interval data.  
Future works could be possible in the following lines.  
Methods other than the _k_-means approach can be employed for efficient anomaly detection.  
An effective method can be proposed to deal with continuous attributes or temporal interval datasets. Table 3 shows acronyms and their full form and purpose.  
**Author Contributions:** Conceptualization, F.A.M. and M.S.; Methodology, F.A.M. and M.S.; Software, F.A.M. and M.S.; Validation, F.A.M. and M.S.; Formal Analysis, F.A.M.; Investigation, F.A.M. and M.S.; Resource, F.A.M. and M.S.; Data Curation, F.A.M. and M.S.; Writing--original draft preparation, F.A.M.; writing--review and editing, F.A.M. and M.S.; visualization, F.A.M. and M.S.; supervision, F.A.M.; project administration, M.S. and F.A.M.; funding acquisition, M.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The corresponding author states that the work does not have any external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data, code and other materials can be made available on request.  
**Conflicts of Interest:** There is no conflict of interest or competing interests among the authors.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{284.5 pt}} \hline \hline
**Acronym** & **Full Form and Purpose** \\ \hline IF & Isolation Forest: It is an anomaly detection using binary tree. \\ \hline SC & Spectral Clustering: It has been used as an outlier detection algorithm many times \\ \hline HDBSCAN & Hierarchical Density-based Spatial Clustering of Applications with Noise: It is a density–based hierarchical clustering approach that has been used for anomaly detection many times with less efficacies \\ \hline ACA & Agglomerative Clustering Algorithm: It is a hierarchical clustering approach for anomaly detection. \\ \hline LOF & Local Outlier Factor: It is an algorithm to identify outliers based on local neighborhood. \\ \hline SSWLOFCC & Streaming Sliding Window Local Outlier Factor Coreset Clustering Algorithm: It focuses on real-time detection of anomalies using big data technologies. \\ \hline PCM & Partitioning Clustering with Merging: It is an algorithm for finding anomalies which uses both partitioning and Hierarchical approaches \\ \hline OnCAD & Online Clustering and Anomaly Detection: It is a clustering-based anomaly detection approach in data streams that considers the temporal as well as spatial proximity of observations to detect the real-time anomaly. \\ \hline MICA & Mixed Clustering Algorithm: It is an algorithm for finding real-time anomalies using both partitioning and Hierarchical approaches \\ \hline RADSPSCA & Real-time Anomaly Detection with Subspace Periodic Clustering Approach is the method proposed in this article. \\ \hline \hline \end{tabular}
\end{table}
Table 3: Acronym table.