BACKGROUND: During the last years, high throughput experimental methods have 
been developed which generate large datasets of protein - protein interactions 
(PPIs). However, due to the experimental methodologies these datasets contain 
errors mainly in terms of false positive data sets and reducing therefore the 
quality of any derived information. Typically these datasets can be modeled as 
graphs, where vertices represent proteins and edges the pairwise PPIs, making it 
easy to apply automated clustering methods to detect protein complexes or other 
biological significant functional groupings.
METHODS: In this paper, a clustering tool, called GIBA (named by the first 
characters of its developers' nicknames), is presented. GIBA implements a two 
step procedure to a given dataset of protein-protein interaction data. First, a 
clustering algorithm is applied to the interaction data, which is then followed 
by a filtering step to generate the final candidate list of predicted complexes.
RESULTS: The efficiency of GIBA is demonstrated through the analysis of 6 
different yeast protein interaction datasets in comparison to four other 
available algorithms. We compared the results of the different methods by 
applying five different performance measurement metrices. Moreover, the 
parameters of the methods that constitute the filter have been checked on how 
they affect the final results.
CONCLUSION: GIBA is an effective and easy to use tool for the detection of 
protein complexes out of experimentally measured protein - protein interaction 
networks. The results show that GIBA has superior prediction accuracy than 
previously published methods.