Recently we planned to conduct a project which applies GIS technologies with region-level statistics to map the incidence and mortality of cervical cancer, as well as Pap smear test results in certain regions of New Brunswick, Canada. By integrating GIS with other analytical technologies such as data mining, spatial analysis and case-control study, we will demonstrate the disease spatial clusters and discover the etiologic hypotheses and significant disease risk factors. Based on our project objectives, the purpose of this literature review is to provide an extensive review and comparison study on existing methodologies used in detecting disease clusters under cancer epidemiological domain and to conclude feasible methodologies for our project. This paper is organized following a study path: (1) data acquisition - issues in cancer data collection; (2) methodologies in data mapping; (3) methodologies in data analysis. It should be noted that this literature review is mainly based on review papers in recent past on following domains: cancer data, disease mapping, statistical methods in spatial analysis, space-time clustering, spatial data mining, and cluster analysis software. The conclusion we made after this extensive review is that spatial data mining is a new, promising way to detect clusters.