Risks clustering in environmental projects (Case study: manufacturing companies)

It is commonly recognized that knowledge is the only source of core competence of Environmental Projects in the knowledge-based companies, but the productivity rate of projects is always Low. Clustering, as a widely used data mining approach, has been used to discover phenotypes from the raw expression data. However traditional clustering algorithms have limitations since they cannot identify the substructures of samples and features hidden behind the data. Different from clustering, blustering is a new methodology for discovering genes that are highly related to a subset of samples. Several blustering models/methods have been presented and used for environmental research. Based on projectʹs characteristics, in this paper, we seek to identify factors influencing on the Environmental Projects Risks Management (EPRM). Finally, we seek to use the Binary Matrix Factorization (BMF) Method for Clustering of the Environmental Projects Risks. It is hoped that this paper will help Environmental Projects managers to implement different corresponding measures. A case study is presented where this model measures and validates at the Lead and zinc Zanjan Company.