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Data mining is the process of finding meaningful patterns in large quantities of data. Traditionally, the subject is introduced through statistics and statistical modeling. This chapter takes an alternative approach that introduces data mining concepts using databases. This perspective presents the important concepts, sidestepping the rigor of theoretical statistics to focus instead on the most important practical aspect: data. The next two chapters extend the discussion begun in this chapter. 11 explains linear regression, a more traditional starting point for modeling, from the perspective of data mining. The final chapter focuses on data preparation. Whether the modeling techniques are within a database or in another tool, data preparation is often the most challenging part of a data mining endeavor. Although earlier chapters have already shown the powerful techniques that are possible using SQL, snobs may feel that data mining is more advanced than mere querying of databases. Such a sentiment downplays the importance of data manipulation, which lies at the heart of even the most advanced techniques. Some powerful techniques adapt well to databases, and learning how they work both in terms of their application to business problems and their implementation on real data provides a good foundation for understanding modeling. Some techniques do not adapt as well to databases, so they require more specialized software. However, the fundamental ideas on using models and evaluating the results remain the same regardless of the sophistication of the modeling technique.



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Although our heuristic discussion of the friction matrix for unentangled polymers demonstratesthe significanceand usefulness of (8.102) or the correspondingEinsteintype formula, in practice, the choice of the intermediatetime scale T~is rather delicate. As we know from the discussion of (4.133),the time scale for the second Rouse mode is 7-/4 hence not very different from that for the slowest mode, T. If we choose and T~ M 7/2,then Q is already changing on the time scale T,, and the second mode is not really fast enough to be considered as noise. One therefore could analyze the decay of the slowest mode and of a few further modes carefully in order to extract the fundamental Rouse time scale with high precision, and the contribution of all the higher modes to any material property is then calculated by means of the well-known Rouse model with the spectrum (4.133). However, in the general spirit of GENERIC, to evaluate (8.102) with rS M 7/2 is the best we can do on the level of a single conformation tensor, without making any assumptions about the dynamics on the coarse level. If better accuracy is required in a systematic approach, several modes need to be taken into account through several conformation tensors. As a general rule, detailed model assumptions should only be used to check the consistency of the coarser level. As long as one works with a single conformation tensor, it may be better to work with the end-to-end vector rather than the slowest mode because the finite extensibility effects become more natural and transparent and because, in contrast to the construction of the slowest mode, this choice does not depend on any properties of the Rouse model. 8.4.3





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Selectivity in acylation, 408 in alkylation, 230, 233, 235, 240, 246, 261, 263, 264 in all ylic oxidation, 485 in aminomethylation, 394 in carboalumination, 331 in carboxylation, 382 in chemical reduction, 648-650 in cross-metathesis of styrene, 697 in Diels-Alder reaction, 333, 334 in electrophilic substitution of aromatics, 580-582 in epoxidation, 450, 454, 456, 459, 523, 524 of Fischer-Tropsch synthesis, 16, 100, 107 in formylation, 414-416 in free-radical chlorination, 586, 587 in halogenation of alkanes, 577, 601, 604 of hydroalanation, 321 in hydroboration, 317, 320, 321 in hydroformylation, 375 in hydrogenation, 625, 626, 628-631, 636, 640,641, 652 in hydrogenolysis, 657, 677, 678 in hydrohalogenation, 291 in hydrosilylation, 324 in hydrostannation, 345 in hydroxylation of aromatics, 494, 501 in hydrozirconation, 326, 327 in isosynthesis, 128 in metathesis, 704 in methane coupling, 110, 112 in methanol synthesis, 115 in methanol to hydrocarbons transformation, 118 in nitration of aromatics, 582 in oxidation of alkanes, 434, 435, 439-441 in ozonolysis of dienes, 481 in polymerization, 742, 743, 759 in Prins reaction, 262 in ring opening of cycloalkanes, 661 in selenofluorination, 345 in side-chain chlorination, 589 in Wacker oxidation, 526 Selectoforming, for reforming, 42 Selenium dioxide allylic oxidation with, 484, 485, 488 bis-hydroxylation with, 470 oxidation of alkynes with, 488, 489 Selfhydrogenation, of ethylene, 623 Shale, 7, 38 Shape selectivity in acylation, 412, 413

where H is some Hermitian positive-definite matrix-valued (as opposed to scalar-valued) function of u .Examples of choices for H [ . ] ,leading to more general forms of adaptive i filters, are treated in the problems at the end of this part (see Probs. V.22, V.23 and V.36).

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