Interpretable knowledge extraction from emergency call data based on fuzzy unsupervised decision tree

Nowadays, call centers are common in different areas of activity providing customer services, medical attention, security services, etc. Each type of call center has its own particularities but all call centers have to plan the availability of resources at each time period to support the incoming calls. The emergency call centers are a special case with extra restrictions. In this context, this work is devoted to providing support for the decision making about resource planning of an emergency call center in order to reach its mandatory quality of service. This is carried out by the extraction of interpretable knowledge from the activity data collected by an emergency call center. A linguistic prediction, categorization and description of the days based on the call center activity and information permits the workload for each category of day to be known. This has been generated by a fuzzy version of an unsupervised decision tree (FUDT), merging decision trees and clustering. This involves quality indexes to reach an adequate trade-off between the tree complexity and the category quality in order to guarantee interpretability and performance. This unsupervised approach deals correctly with the real management of this type of centers generating and preserving expert knowledge.