Only one question in the end.

Clustering algorithm:

- Clustering

- k-means

- OPTICS

Dimensionality Reduction

In the field of machine learning, it is useful to apply a process called dimensionality reduction to highly dimensional data. The purpose of this process is to reduce the number of features under consideration, where each feature is a dimension that partly represents the objects. Why is dimensionality reduction important? As more features are added, the data becomes very sparse and analysis suffers from the curse of dimensionality. Additionally, it is easier to process smaller data sets. Dimensionality reduction can be executed using two different methods:

• Selecting from the existing features (feature selection)
• Extracting new features by combining the existing features (feature extraction)

The main technique for feature extraction is the Principle Component Analysis (PCA). PCA guarantees finding the best linear transformation that reduces the number of dimensions with a minimum loss of information. Sometimes the information that was lost is regarded as noise - information that does not represent the phenomena we are trying to model, but is rather a side effect of some usually unknown processes. PCA process can be visualized as follows (Figure 3):

Data Audit Logs Detect Suspicious Activity Prepare Data Build Virtual Peer Groups Detect Suspicious Activity Build Give Virtual Detect Clean Data User-Folder Dimensionality Clustering Permissions Suspicious Matrix Reduction to Clusters File Access Build Virtual Peer Groups Dimensionality Clustering Reduction