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extract a cluster C from the set of clusters; k = 0; for i = 1 to nt do let C0i , C1i be the two clusters obtained from C by bisecting C using standard k-means (k = 2); if (i = 1) then s = sse({C0i , C1i }); if (sse({C0i , C1i }) s) then k = i; s = sse({C0i , C1i }); endif; endfor; add C0k , C1k to set of clusters; endwhile } The cluster C that is bisected may be the largest cluster, or the cluster having the largest sse. Evaluate the time performance of bisecting k-means compared with the standard k-means and with some variant of a hierarchical clustering. 15. One of the issues that the k-means algorithm must confront is that the number of clusters k must be provided as an input parameter. Using clustering validity design an algorithm that identi es local maxima of validity (as a function of k) to provide a basis for a good choice of k. For a solution that applies to image segmentation, see the work by Ray and Turi.

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Within each category is an entry form formatted specifically for that category, so for example if you want to know the score of today s Mets baseball game, you choose Major League Baseball as the sport and Mets as the team Similarly if you want driving directions, enter the starting and ending address..

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There are some technological plateaus that can be glimpsed from a great distance and forecast fairly accurately but that come so clearly into view as to gain immediacy and irresistibility only after a journey of many years. Such an end point is the one discussed in this book, the replacement of copper by optical ber for xed communication across the last mile, the so-called access component of the telecommunications infrastructure. The completion of the evolving ber infrastructure to include the access links has been forecast for years. However, until recently the various tentative engineering studies, eld demonstrations, and other gestures toward an all- ber future were succeeded by very little follow-through. Now ber to the home (FTTH) is happening. And it is happening at many places around the globe. A combination of circumstances has caused this change. The growing bandwidth needs of individual users on both sides of the economic digital divide have combined with the development needs of individual communities and indeed entire nations to constitute a considerable user pressure. Quantitatively, these pressures for more ease of use and more bandwidth to more users are rapidly exceeding the capability of even the most highly evolved copper-based or wireless solutions. This bandwidth bottleneck has been understood for years. What has perhaps been the most immediate new triggering event for today s long-awaited FTTH movement, at least in the United States, has been the threat to the continued survival of the established local access carriers posed by competition from cable technology and, to a lesser extent, from cellular radio. These are some of the things that are pushing the movement toward FTTH, and we shall spend the rest of this chapter dealing with them. But there are also things

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Despite the efforts of the preceding section regarding reflectio of C++ BoostMultiArray types into Python, in practice, when working in Python, the authors have found the facilities of NumPy arrays to be far more convenient (NumPy was mentioned briefl in section 12) Specificall , their notational conveniences and the large body of functionality provided by the NumPy library motivates their use in Python beyond the argument of C++ interoperability Indeed, when working in C++, a library dedicated to scientifi manipulation of arrays such as Blitz++2 wins the authors favour for such work over lower-level container types like native C arrays or BoostMultiArray types But now to the crux of the matter If we haven t made this point earlier then we ll make it for the firs time now.

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