  Wearable and IoT devices requiring positioning and localisation services grow
in number exponentially every year. This rapid growth also produces millions of
data entries that need to be pre-processed prior to being used in any indoor
positioning system to ensure the data quality and provide a high Quality of
Service (QoS) to the end-user. In this paper, we offer a novel and
straightforward data cleansing algorithm for WLAN fingerprinting radio maps.
This algorithm is based on the correlation among fingerprints using the
Received Signal Strength (RSS) values and the Access Points (APs)'s identifier.
We use those to compute the correlation among all samples in the dataset and
remove fingerprints with low level of correlation from the dataset. We
evaluated the proposed method on 14 independent publicly-available datasets. As
a result, an average of 14% of fingerprints were removed from the datasets. The
2D positioning error was reduced by 2.7% and 3D positioning error by 5.3% with
a slight increase in the floor hit rate by 1.2% on average. Consequently, the
average speed of position prediction was also increased by 14%.
