Abstract:
Extracting valuable information from digital data is known as Knowledge Discover in Databases (KDD) and objective of Information Systems (IS) research. Data Mining (DM), the extraction and verification of hidden patterns in data, is the core activity of the KDD process. Literature relating to DM is strongly focused on scientific rigor of the research, however, by contrast part of the IS community finds practical relevance lacking in DM research. Based on a literature review of DM research paradigms, this article develops a data mining approach based on design science theory. The application of this approach on a real-case business problem reveals the acceptance of different forms of data and the consideration of the interactive and iterative nature of DM key success factor for DM projects. Moreover, business needs are confining the necessary depth of DM activities. Building on the evaluation of the model within the case study, we suggest the results might point out an explicit connection between business and science especially in the field of DM. This paper provides a contribution to both the DM professionals and the DM community in IS research. Further, our approach should be examined and verified for other DM related practical cases as well, as it might contribute to a unifying theory of DM based on design science research.
