document: On mining cross-graph quasi-cliques Joint mining of multiple data sets can often discover interesting , novel , and reliable patterns which can not be obtained solely from any single source . For example , in cross-market customer segmentation , a group of customers who behave similarly in multiple markets should be considered as a more coherent and more reliable cluster than clusters found in a single market . As another example , in bioinformatics , by joint mining of gene expression data and protein interaction data , we can find clusters of genes which show coherent expression patterns and also produce interacting proteins . Such clusters may be potential pathways . In this paper , we investigate a novel data mining problem , mining cross-graph quasi-cliques , which is generalized from several interesting applications such as cross-market customer segmentation and joint mining of gene expression data and protein interaction data . We build a general model for mining cross-graph quasi-cliques , show why the complete set of cross-graph quasi-cliques can not be found by previous data mining methods , and study the complexity of the problem . While the problem is difficult , we develop an efficient algorithm , Crochet , which exploits several interesting and effective techniques and heuristics to efficaciously mine cross-graph quasi-cliques . A systematic performance study is reported on both synthetic and real data sets . We demonstrate some interesting and meaningful cross-graph quasi-cliques in bioinformatics . The experimental results also show that algorithm Crochet is efficient and scalable .
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