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Data mining projects are complex and have a high failure rate. In order to improve project management and success rates of such projects a life cycle is vital to the overall success of the project. This paper reports on a research project that was concerned with the life cycle development for large scale data mining projects. The paper provides a detailed view of the design and development of a generic data mining life cycle called DMLC. The life cycle aims to support all members of data mining project teams as well as IT managers and academic researchers and may improve project success rates and strategic decision support. An extensive analysis of eight existing life cycles leads to a list of advantages, disadvantages, and characteristics of the life cycles. This is extended and generates a conglomerate of several guidelines which serve as the foundation for the development of a new generic data mining life cycle. The new life cycle is further developed to incorporate process, people and data aspects. A detailed study of the human resources involved in a data mining project enhances the DMLC.
Hoffmann, Markus and Tierney, Brendan, "Development of an Enhanced Generic Data Mining Life Cycle (DMLC)" (2009). Conference papers. 296.