A popular way for using radio-frequency (RF) signals (e.g. WiFi) to position people or device indoors is by matching received radio signal strength (RSS) to fingerprints that are spatial signatures of such measures. Traditionally such signal maps are built by manual collection of repeated measurements at predefined locations following a spatial sampling scheme. Recently, such labor intensive processes are being replaced by robot-based automation or crowd-sourced simultaneous localization and mapping (SLAM). These new approaches produce time-stamped trajectories along with time-stamped RSS as the human or robot moves freely about the building. However, they require an additional procedure to segment the continuous RF samples into fingerprint cells to produce a robust signal map. In this paper, we explore several strategies for building optimal signal maps from RSS collected along robotic or pedestrian trajectories. We compare two clustering algorithms with a baseline strategy that divides the trajectories into a hierarchy of fixed-size grids. We study the trade-off between the spatial extent of the fingerprint cells and the differentiability of the RSS distribution in each cell, as well as their impact on localization accuracy and on fingerprint storage. We experimented with traces collected by an autonomous robot exploring a large multi-floor office building.