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  algorithmI 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?
Scalar entities for k means clustering
classification;clustering;k means;feature extraction;unsupervised learning