By David L. Olson (auth.)
This e-book bargains an outline of information administration. It begins with an creation to the topic, putting descriptive types within the context of the general box in addition to in the extra particular box of knowledge mining research. bankruptcy 2 covers information visualization, together with instructions for having access to R open resource software program (described via Rattle). either R and Rattle are loose to scholars. bankruptcy three then describes marketplace basket research, evaluating it with extra complicated types, and addresses the idea that of carry. hence, bankruptcy four describes smarketing RFM versions and compares it with extra complex predictive types. subsequent, bankruptcy five describes organization principles, together with the APriori set of rules and offers software program aid from R. bankruptcy 6 covers cluster research, together with software program aid from R (Rattle), KNIME, and WEKA, all of that are open resource. bankruptcy 7 is going directly to describe hyperlink research, social community metrics, and open resource NodeXL software program, and demonstrates hyperlink research software utilizing PolyAnalyst output. bankruptcy eight concludes the monograph.
Using business-related facts to illustrate types, this descriptive e-book explains how tools paintings with a few citations, yet with no particular references. the knowledge units and software program chosen are commonly on hand and will simply be accessed.
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Extra resources for Descriptive Data Mining
What needs to be done is to identify the portion of the population to send promotional materials to. For instance, if a promotion (such as a coupon) costing $20 is proposed, we need information available to calculate an average profit per profile. 12 shows these calculations. 2 reaches its maximum with the fourth segment. It is clear that the maximum profit is found by sending to the five most responsive segments of the ten in the population. The implication is that in this case, 40 3 Market Basket Analysis promotional materials should be sent to these segments.
3. 333 0 Soda products), things that would be expected. It appears that it would not make sense to seek cross-sales of milk or soda to beer drinkers. This information could be actionable in a negative sense to advertisers (or may be beer drinkers just haven’t thought of the value of milk in combination with beer). On the other hand, it would make sense to push potato chips to beer drinkers. Some combinations, such as between milk and soda, may not include any actionable content, even though the statistical relationship may be quite strong.
Lift maxes out at Group 554, the 73rd of 125 cells. 739. Of course, the point is not to maximize lift, but to maximize profitability, which requires knowing expected profit rate for revenue, and cost of marketing. 792. Value Function The value function compresses the RFM data into one variable—V = M/R. 1), the analysis is simplified to one dimension. 8. 2 shows lift by value ratio. 2, the most responsive segment has an expected return of slightly over 40%. The lift line is the cumulative average response as segments are added (in order of response rate).
Descriptive Data Mining by David L. Olson (auth.)
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