A massive rise in the number and expenditure of pathology ordering by general practitioners (GPs) concerns the government and attracts various studies with the aim to understand and improve the ordering behavior. In this paper we attempt to understand the reasons for and implications of pathology ordering by general practitioners by applying an unsupervised text mining technique on the clinical notes of the pathology requests obtained from a pathology company in Australia. Pathology requests are clustered into different groups based on the information that is included by the doctors in clinical notes accompanying the requests. Features and patterns of the groups are investigated and analyzed. The novelty of the paper is in using text mining techniques to extract knowledge from unstructured text data in the area of pathology ordering and to understand the reasons for pathology ordering from a doctors? perspective.