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In this paper, we propose a greedy clustering algorithm to identify groups of related genes and a new measure to improve the results of this algorithm. Clustering algorithms analyze genes in order to group those with similar behavior. Instead, our approach groups pairs of genes that present similar positive and/or negative interactions. In order to avoid noise in clusters, we apply a threshold, the neighbouring minimun index(λ), to know if a pair of genes have interaction enough or not. The algorithm allows the researcher to modify all the criteria: discretization mapping function, gene-gene mapping function and filtering function, and even the neighbouring minimun index, and provides much flexibility to obtain clusters based on the level of precision needed. We have carried out a deep experimental study in databases to obtain a good neighbouring minimun index, λ. The performance of our approach is experimentally tested on the yeast, yeast cell-cycle and malaria datasets. The final number of clusters has a very high level of customization and genes within show a significant level of cohesion, as it is shown graphically in the experiments.