Customer　churn　has　emerged　as　a　critical　issue　for　Customer　Relationship　Management　and　customer　retention　in　the　telecommunications　industry,　thus　churn　prediction　is　necessary　and　valuable　to　retain　the　customers　and　reduce　the　losses.　Recently　rule-based　classification　methods　designed　transparently　interpreting　the　classification　results　are　preferable　in　customer　churn　prediction.　However　most　of　rulebased　learning　algorithms　designed　with　the　assumption　of　well-balanced　datasets,　may　provide　unacceptable　prediction　results.　This　paper　introduces　a　Fuzzy　Association　Rule-based　Classification　Learning　Algorithm　for　customer　churn　prediction.　The　proposed　algorithm　adapts　CAIM　discretization　algorithm　to　obtain　fuzzy　partitions,　then　searches　a　set　of　rules　using　an　assessment　method.　The　experiments　were　carried　out　to　validate　the　proposed　approach　using　the　customer　services　dataset　of　Telecom.　The　experimental　results　show　that　the　proposed　approach　can　achieve　acceptable　prediction　accuracy　and　efficient　for　churn　prediction.　©　Springer　International　Publishing　Switzerland　2016.