Scalar entities for k means clustering I am trying to understand kmeans clustering and I read a article where kmeans is used for clustering the features generated in network logs. This clustering is followed by a supervised classification.

They mention in article :

"Due to extensive repetition of input and output network ip addresses, portnumbers, and protocol information in the collected data, these were deemed scalar entities for analysis and excluded while clustering. The remaining attributes  were chosen as input to clustering  algorithm"

I fail to understand what scalar entities mean here? Does this mean that these input and output ip address and port numbers are not considered as features during feature extraction stage? If the ip addresses arent considered, how are the features mapped to the input data of network logs?
While doing clustering analysis, you would prefer to have features which discriminate data points and help you discover more homogeneous segments. However, if the value of a particular feature or variable is same for large percentage of the data, then that feature would not add any information for clustering.

Let us take an example, say you are dealing with bank customers data and there's a feature called 'country' in it. If in the given data, most of the customers have country as USA, then you would want to simply drop that feature as it adds no useful information. This is the extreme case in which there is single unique value.

In your network data logs example, port numbers and protocols would be mostly the same. The web server might be listening on port 443 for HTTPS protocol connection. So in that case, the two columns can be excluded from clustering analysis.