Document Type
Conference Paper
Rights
Available under a Creative Commons Attribution Non-Commercial Share Alike 4.0 International Licence
Disciplines
Computer Sciences
Abstract
Data mining projects are complex and can 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 data mining projects, its team members and their role. The paper provides a detailed view of the design and development of the 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 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. A detailed study of the human resources involved in a data mining project enhances the DMLC.
DOI
https://doi.org/1109/CIDM.2009.4938637
Recommended Citation
M. Hofmann and B. Tierney, "An enhanced data mining life cycle," 2009 IEEE Symposium on Computational Intelligence and Data Mining, Nashville, TN, USA, 2009, pp. 109-117 , doi: 10.1109/CIDM.2009.4938637.
Publication Details
IEEE Symposium on Computational Intelligence and Data Mining 2009