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The source node S sends the message toward the geocasting region, using the GFG algorithm [12]; Each node inside the region retransmits the message when receiving it for the rst time, and ignores it when receiving it again; Each internal border node (node inside a region having neighbor(s) on planar graph outside the region) will instruct (together with retransmission) all its perimeter neighbors outside the region to perform right-hand-based face traversals; Each external border node (node outside the region having neighbor(s) on the planar graph inside the region) will initiate right-hand-based face traversal(s) with respect to all edges leading to internal-perimeter neighbors, after receiving the rst copy of the message, and will ignore further received copies unless a packet is received from an external neighbor following a different external face (in which case it forwards it along that face, as requested). Each traversal is performed until another node that is inside the region is found.

MythGallery is a plug-in that enables you to view your digital photos. MythGallery organizes a collection of on-disk images for display as an index of thumbnail images or a moving slide show (see Figure 9-3). MythGame is a plug-in that enables you to run different emulators and games from within MythTV. MythGame provides the interface between Linux-based game emulators (such as Xmame, Xmess, Snes9x, and others) so you can play old-school arcade games within MythTV (see Figure 9-4). See www.mameworld.net for more information on Mame.

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The case study in this chapter is carried out using the Cross-Industry Standard Process for Data Mining (CRISP DM). According to CRISP DM, a given data mining project has a life cycle consisting of six phases, as illustrated in Figure 7.1. Note that the phase sequence is adaptive. That is, the next phase in the sequence often depends on the outcomes associated with the preceding phase. The most signi cant dependencies between phases are indicated by the arrows. For example, suppose that we are in the modeling phase. Depending on the behavior and characteristics of the model, we may have to return to the data preparation phase for further re nement before moving forward to the model evaluation phase. The six phases are as follows: 1. Business understanding phase. The rst phase in the CRISP DM standard process may also be termed the research understanding phase. a. Enunciate the project objectives and requirements clearly in terms of the business or research unit as a whole. b. Translate these goals and restrictions into the formulation of a data mining problem de nition. c. Prepare a preliminary strategy for achieving these objectives.

(2 ) 1/2 (a) l1, 2 63:65, 81:55, (b) l1, 2 70:57, 84:43, (c) l1, 2 77:74, 89:46 (a) 9.16, (b) l1, 2 8:46, 9:86 (a) l1, 2 1072, 1128, (b) l1, 2 1340, 6218 and l1 1478 384

Lerman, C (1997) Translational behavioral research in cancer genetics Preventive Medicine, 26, S65 S69 Lerman, C, Caporaso, N, Audrain, J, Main, D, Bowman, E, Lockshin, B, et al (1999) Evidence suggesting the role of specific genetic factors in cigarette smoking Health Psychology, 18, 14 20 Lerman, C, Caporaso, N, Main, D, Audrain, J, Boyd, N R, Bowman, E D, et al (1998) Depression and self-medication with nicotine: The modifying influence of the dopamine D4 receptor gene Health Psychology, 17, 56 62 Lerman, C, Daly, M, Sands, C, Balshem, A, Lustbader, E, Heggan, T, et al (1993) Mammography adherence and psychological distress among women at risk for breast cancer Journal of the National Cancer Institute, 85, 1074 1080 Lerman, C, Hughes, C, Lemon, S, Main, D, Snyder, C.

For example, Spoon Size Shredded Wheat has x1 = 0 grams of sugar, x2 = 3 grams of ber, and a nutritional rating of 72.8018. The estimated regression equation would predict, however, that the nutritional rating for this cereal would be y = 51.787 2.2090(0) + 2.8408(3) = 60.3094 Therefore, we have a residual for Spoon Size Shredded Wheat of y y = 72.8018 60.3094 = 12.4924, illustrated in Figure 3.3. Each observation has its own residual, which, taken together, leads to the calculation of the sum of squares error as an overall measure of the estimation errors. Just as for the simple linear regression case, we may again calculate the three sums of squares as follows: SSE = SSR = SST = (y y )2 ( y y )2 (y y )2

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