For last several years, much research has been dedicated to the development of a reliable infrastructure for mobile ad hoc networks. A hierarchal approach has been a major player, in this context, especially its clusterhead based scheme. Many techniques have been proposed for the clustering and selection of clusterheads in mobile ad hoc networks. However, almost all techniques use only a single quality measure to distinguish between the capabilities of the nodes in the selection of clusterheads. This bounds the efficiency of the selection process and degrades network performance. In this paper, we present a scalable clustering approach that can generate customizable clustering techniques with as many quality measures as desired. Empirical results show that our clustering method experiences significant improvement on network performance compared to single measure clustering with reductions of up to 31% and 46% on average number of clusterhead switches and energy standard deviation, respectively.