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?