The important need for suitable indoor positioning systems has recently seen an exponential rise with location-based services emerging in many sectors of human life. This has led to adopting techniques to mine location data to discover useful insights to improve the accuracy of the various indoor positioning systems. Although indoor positioning has been reviewed in some literary works, an in-depth survey of how data mining could improve the performance of indoor localization systems is still lacking. This paper surveys data mining techniques such as Na¨ıve Bayes, Regression, K-Means, K-Nearest Neighbor (KNN), Support Vector Machines (SVM), Random Forest (RF), Expectation Maximization (EM), Neural Networks (NN), and Deep Learning (DL) including how they were used to improve the accuracy of indoor positing systems using various supporting technologies such as WiFi, Bluetooth, Radio Frequency Identification (RFID), Visible Light Communication (VLC), and indoor localization techniques such as Received Signal Strength Index (RSSI), Channel State Information (CSI), fingerprinting, and Time of Flight (ToF). Additionally, we present some of the challenges of existing indoor positioning systems that employ data mining while highlighting areas of future research that could be exploited in addressing those challenges.