Fun at a Department Store: Data Mining Meets Switching Theory Author(s): Anna Bernasconi, Valentina Ciriani, Fabrizio Luccio, Linda Pagli Keywords: SOP, Implicants, Data Mining, Frequent Itemsets, Blulife
Abstract:
In this paper we introduce new algebraic forms, SOP + and DSOP + , to represent functions f:{0,1}n → ℕ, based on arithmetic sums of products. These expressions are a direct generalization of the classical SOP and DSOP forms.
We propose optimal and heuristic algorithms for minimal SOP + and DSOP + synthesis. We then show how the DSOP + form can be exploited for Data Mining applications. In particular we propose a new compact representation for the database of transactions to be used by the LCM algorithms for mining frequent closed itemsets.
A new technique for extracting associations between items present (or absent) in transactions (sales transactions).
Of interest to people with the funds to pay for data mining and topic maps.
Topic maps are useful to bind the mining of such associations to other information systems, such as supply chains.
Questions:
- How would you use data mining of transaction associations to guide collection development? (3-5 pages, with citations)
- How would you use topic maps with the mining of transaction associations? (3-5 pages, no citations)
- How would you bind an absence of data to other information? (3-5 pages, no citations)
Observation: Intelligence agencies recognize the absence of data as an association. Binding that absence to other date is a job for topic maps.
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