Recent developments in information technology paved the way for the collection of large amounts of data pertaining to various aspects of an enterprise. The greatest challenge faced in processing these massive amounts of raw data gathered turns out to be the effective management of data with the ultimate purpose of deriving necessary and meaningful information out of it. The following paper presents an attempt to illustrate the combination of visual and analytical data mining techniques for planning of marketing and production activities. The primary phases of the proposed framework consist of filtering, clustering and comparison steps implemented using interactive pie charts, K-Means algorithm and parallel coordinate plots respectively. A prototype decision support system is developed and a sample analysis session is conducted to demonstrate the applicability of the framework.
Ertek, G., Kuruca, C., Aydin, C., Erel, B.F., Dogan, H., Duman, M., Ocal, M., and Ok, Z.D. (2004). “Visual and analytical mining of sales transaction data for production planning and marketing.” 4th International Symposium on Intelligent Manufacturing Systems, Sakarya, Turkey.
Note: This is the final draft version of this paper. Please cite this paper (or this final draft) as above.
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