By David L. Olson (auth.)
ISBN-10: 9811033390
ISBN-13: 9789811033391
ISBN-10: 9811033404
ISBN-13: 9789811033407
This ebook deals an summary of information administration. It starts off with an advent to the topic, putting descriptive versions within the context of the final box in addition to in the extra particular box of information mining research. bankruptcy 2 covers info 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 complex types, and addresses the idea that of carry. in this case, bankruptcy four describes smarketing RFM types and compares it with extra complex predictive versions. subsequent, bankruptcy five describes organization ideas, together with the APriori set of rules and gives 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 versions, this descriptive publication explains how equipment paintings with a few citations, yet with out designated references. the knowledge units and software program chosen are commonly to be had and will simply be accessed.
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Sample text
2 Profit impact of lift The purpose of lift analysis is to identify the most responsive segments. Here the greatest lift is obtained from the first segment, with the next five contributing notable response. We are probably more interested in profit, however. We can identify the most profitable policy. 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.
The data set contained order (or donation) history and order dollars (or donation amounts) per each customer (or donor), which were used for F (F1, F2, F3, F4, F5) and M (M1, M2, M3, M4, M5). We also included one response variable (Yes or No) to the direct marketing promotion or campaign. 1. 01 level. The relationship between R and response is negative, as expected. In contrast, F and M are positively associated with customer response. R and F are stronger predictors for customer response. Dividing the data into 125 cells, designated by 5 categories for each of the three groups, the most attractive group would be 555, or Group 5 for each of the 3 variables.
3 displays the counts obtained for these 125 cells. 4. 1 and minimum support of 50. Italicized numbers indicate those cells with support below 50. 3 Count by RFM cell—training set RF 55 54 53 52 51 45 44 43 42 41 35 34 33 32 31 25 24 23 22 21 15 14 13 12 11 R R 12–305 R 306–687 R 688–1290 R 1291–1943 R 1944+ Totals F F 6+ F 4–5 F3 F2 F1 F 6+ F 4–5 F3 F2 F1 F 6+ F 4–5 F3 F2 F1 F 6+ F 4–5 F3 F2 F1 F 6+ F 4–5 F3 F2 F1 M1 M2 M3 0 2 9 142 2425 0 0 6 150 2755 0 1 9 163 2951 0 0 9 211 3377 0 1 9 268 4145 16,623 0 18 94 616 1978 1 16 88 707 2339 1 16 88 697 2567 0 22 95 749 2704 0 11 122 878 3177 16,984 16 118 363 1012 1386 11 87 316 1046 1699 5 122 319 1002 1645 9 72 290 1096 1660 3 74 261 1108 1641 15,361 M4 151 577 756 1135 938 101 510 699 1140 1067 70 420 706 1128 1078 56 372 678 1128 1108 22 243 511 995 908 16,497 M5 1761 1157 671 559 387 1018 927 636 616 416 799 832 589 645 437 459 688 501 561 478 170 409 380 522 449 16,067 In the training set, 10 of 125 possible cells were empty, even with over 80,000 data points.
Descriptive Data Mining by David L. Olson (auth.)
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