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clusters (k), initially k observations, referred to as seeds, are selected. This initial assignment may be randomly generated, or it may be selected with some knowledge of the content. Following the selection of the k cluster seeds, all other observations in the data set are assigned to the seed closest to it, based on the distance between the observation and each of the seeds. Any distance measure, such as those discussed in Section 3.2.2, can be used to measure the distance between pairs of observations. This generates an initial grouping of the data into k clusters. The next step is to optimize the groupings. A mean value, or centroid, is calculated for each of the groups across all the variables. All observations are then reassigned to the cluster whose centroid they are closest to. At this point an assessment should be made as to whether the grouping is optimal or not. If the assessment is that the grouping is optimal, then the clustering is complete. If not, the process of calculating the cluster means and then reassigning all observations is repeated. The assessment of an optimal solution is based on whether any observations are reassigned to a different cluster or based on an error criterion (Err): Err

dv + 1 e a(t v)dz1(v)

The agglomerative hierarchical approach to clustering starts with each observation as its own cluster and then continually groups the observations into increasingly larger groups. This results in a hierarchical organization of the data which can be used to characterize the data set, and from this hierarchy speci c groups can be selected. This method is useful and a widely used approach to analyzing data; however, the analysis can be time-consuming, and it is usually limited to smaller data sets because the method requires the complete distance matrix to be calculated. Like all clustering approaches, the rst step in performing agglomerative hierarchical clustering is to select the variables and observations to cluster. For reasons discussed earlier, the data should also be normalized. Table 3.14 shows ve observations: A, B, C, D, and E. Each observation is measured over

TABLE AP6.1-8

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So far we have seen how the SRLG information is used in the CSPF computation How does a router find out about the SRLG membership From an implementation point of view, there are two options on how this knowledge can be provided to the routers in the network In both cases, the assumption is that the network operator knows which resources share what risks and can build a database of SRLG membership The first option for distributing this information to the routers is to configure the SRLG information on the routers The second option is to use the IGP to distribute SRLG information similarly to how other TE information is carried in the IGP (Such extensions were defined for OSPF in the context of Generalized Multi-Protocol Label Switching, or GMPLS, extensions for OSPF.

Probably the most critical cooling component of your system is your CPU heat sink/fan assembly. In particular, the interface between your CPU and heat sink is the most critical, micro-fine adjustment that you can make. Many stock CPU heat sinks (even some of the expensive/pretty ones) simply do not have a mirrorsmooth finish. This is critical for an efficient CPU to heat sink heat conduction and cooling. This is even more critical in a PVR system because you re trying to do more with less (more cooling with less fan rotations and noise). To get the absolute best performance out of your heat sink/CPU cooling system, clean and hone, or lap, your heat sink surface (where the CPU touches it) to a mirror finish using progressively finer and finer grits of abrasives until you can see your reflection in it. Make sure you definitely cannot see any patterned groves, scratches, or surface lines. Then you can go back and apply a little heat sink compound, install the heat sink/fan assembly, and you ll now have the best CPU/sink interface possible; resulting in a CPU temperature that is 10 20 (F) cooler than before. For tips on CPU heat sink lapping, see this overclocker site: http://overclockers club.com/guides/heatsinklappingguide.php.

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