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Then, Z consists of the rst r 1 symbols of W, so Z = Z If m < q, then m is the second highest index of a symbol in W and q is the highest index of a symbol in W, so U = U and V = V Example 5 Consider the Rymon tree of the collection P({i1 , i2 , i3 , i4 ) shown in Figure 72 The set i1 i3 i4 is the union of the sets i1 i3 and i1 i4 that have the common ancestor i1 Next we discuss an algorithm that allows us to compute the collection FT of all -frequent item sets for a transaction data set T The algorithm is known as the Apriori algorithm.



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To understand how costs and revenues interact to determine economic profits or losses, economists like to break up a firm s total costs into two subcategories: fixed costs and variable costs.





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We begin with the procedure apriori gen that starts with the collection FT,k of frequent item sets for the transaction data set T that contain k elements and generates a collection Ck+1 of sets of items that contains FT,k+1 , the collection the frequent item sets that have k + 1 elements The justi cation of this procedure is based on the next statement Theorem 3 Let T be a transaction data set over a set of items I and let k N such that k > 1 If W is a -frequent item set and |W| = k + 1, then there exist a -frequent item set Z and two items im and iq such that |Z| = k 1, Z W, W = Z {im , iq } and both Z {im } and Z {iq } are -frequent item sets..

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Such analysis would appear to offer some level of comfort since, by definition, numbers are numbers. But, Rubin cautions against so simplistic an approach, citing state-to-state comparisons often seen in newspapers as an example. These, he contends, are misleading because many factors may intervene: murder rates differ by income, age, racial composition, population density, and other factors; and probability of arrest also differs and obviously affects rates and subsequent punishment. Another factor often overlooked is that causality can go either way. A given state may have a capital punishment statute precisely because it has a higher murder rate. As Rubin notes, In such a case, observing capital punishment and a high murder rate says nothing about causality, and the deterrence argument is that rates would be even higher if there were no capital punishment. 3 Such observations may initially be disheartening when one thinks of the much broader, more frequent, and less serious issue of occupational fraud. If the best and brightest cannot achieve any consensus around the meaning of statistics relating to murder and capital punishment, what earthly chance do we have with occupational fraud Upon reflection, however, we may have a few advantages as we think about research concerning punishment and deterrence:

(i.e., where u is a unit vector), is Q Q P y y T cos 2 U cos U T 2 U T U T T U U T T y U U T r1 sin y=2 U T u1 sin U T T 2 U def T U T U y q r T U T U; T r2 sin y=2 U T y U U T u2 sin U T U T T 2 U y U T U T S T R U r3 sin y=2 R y S u3 sin y 2 P

non-exponential waiting times, 176 queuing applications, 176 survival analysis, 176 pointwise prediction bounds, 94 Poisson regression model, 4 posterior distribution, 90 high-dimensional, 114 histogram of samples, 94, 110 incomplete data, 189 joint, 107 prior distribution beta, 99 comparison of, 122 conjugate, 91 exponential, 99 gamma, 99 Jeffreys , 91, 99 noninformative, 91, 122, 126 reference, 91 uniform, 98, 105 uniform, wide, 163 Project Evaluation and Review Technique, 175 proportional hazard, 2 pure birth process, see Markov process pure death process, see Markov process queue arrival, 208 busy period, 208, 231 cellular telephone network, see cellular telephone network customer, 207 incomplete data, 215 in nite state, 238 Bayes predictive density, 242 nonstationary, 242 nonstationary predictive distribution, 244 posterior probabilities, 243 predictive density, mixed, 244 stationary, 241 stationary predictive distribution, 243 transmittance, 238 limiting distribution, 210, 231 Little s law, 210 machine-repairman problem, 207 mean time to total failure, 211 M/G/1, 229 Bayes predictive density, 235 Bayesian argument, 233 owgraph, 231 kernel density estimate, 235 likelihood, 234

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The MAC algorithms taxonomy is summarized in the following table:

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