Abstract
This paper initially describes the relational counterpart of possibilistic c -means (PCM) algorithm, called relational PCM (or RPCM). RPCM is then improved to better handle arbitrary dissimilarity data. First, a re-scaling of the PCM membership function is proposed in order to obtain zero membership values when the distance to prototype equals the maximum value allowed in bounded dissimilarity measures. Second, a heuristic method of reference distance initialisation is provided which diminishes the known PCM tendency of producing coincident clusters. Finally, RPCM improved with our initialisation strategy is tested on both synthetic and real data sets with satisfactory results.
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