Flow cytometry (FCM) involves the use of optical and fluorescence measurements of the characteristics of individual biological cells, typically in blood samples. It is a widely used standard method of analysing blood samples for the purpose of identifying and quantifying the different types of cells in the sample, the result of which are used in medical diagnoses. The multidimensional dataset obtained from FCM is large and complex, so it is difficult and time-consuming to analyse manually. The main process of differentiation and therefore labelling of the populations in the data which represent types of cells is referred to as Gating: gating is the first step of FCM data analysis and highly subjective. Significant amounts of research have focussed on reducing this subjectivity, however a faster standard gating technique is still needed. Existing automated gating techniques are time-consuming or need many user-defined parameters which affect the differentiation to different clustering results. This thesis presents and discusses FLOPTICS: a novel automated gating technique that is a combination of density-based and grid-based clustering algorithms. FLOPTICS has an ability to classify cells on FCM data faster and with fewer user-defined parameters than many state-of-the-art techniques, such as FlowGrid, FlowPeaks, and FLOCK.