The advances in technologies for obtaining multi-omic data provide different levels of molecular information that progressively increase in volume and variety. This study proposes a methodology for integrating clinical and multiomic data, which aim is the identification of cancer subtypes using fuzzy clustering algorithm, representing the different degrees between molecular profiles. A better characterization of tumors in molecular subtypes can contribute to a more personalized and assertive medicine. A classifier that uses a target class from literature results indicates which omic data sets should be integrated. Next, data sets are pre-processed to reduce high dimensionality. The selected data is integrated and then clustered. The fuzzy C-means algorithm was chosen due to its ability to consider the shared patients characteristics between different groups. As a case study, colorectal cancer (CRC) data were used. CCR has the fourth highest incidence in the world population and the third highest in Brazil. Methylation, miRNA and mRNA expression data were extracted from The Cancer Genome Atlas (TCGA) project portal. It was observed that the addition of miRNA expression and methylation data to a literature mRNA expression classifier increased its accuracy by 5 percentage points. Therefore, methylation, miRNA and mRNA expression data were used in this work. The attributes of each data set were pre-selected, obtaining a significant reduction in the number of attributes. Groups were identified using the fuzzy C-means algorithm. The variation of the hyperparameters of this algorithm, number of groups and membership degree, indicated the best performance combination. This choice considered the effect of parameters variation on biological characteristics, especially on the overall survival of patients. Clusters showed that patients considered not grouped had biological characteristics shared between groups of different prognoses. The combination of clinical and omic data to better predict the phenotype revealed promissing results.