There are two approaches to speaker diarization. They are bottom-up and top-down. Our work on top-down systems show that they can deliver competitive results compared to bottom-up systems and that they are extremely computationally efficient, but also that they are particularly prone to poor model initialisation and cluster impurities. In this paper we present enhancements to our state-of-the-art, top-down approach to speaker diarization that deliver improved stability across three different datasets composed of conference meetings from five standard NIST RT evaluations. We report an improved approach to speaker modelling which, despite having greater chances for cluster impurities, delivers a 35% relative improvement in DER for the MDM condition. We also describe new work to incorporate cluster purification into a top-down sys- tem which delivers relative improvements of 44% over the baseline system without compromising computational efficiency.