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< xml version 1.0 > <env:Envelope xmlns:env http://www.w3.org/2003/05/soapenvelope > <env:Header env:mustUnderstand true env:role http:// www.w3.org/2003/05/soap-envelope/role/next > <ca:CacheControl xmlns:ca http://example.org/2001/06/ cache > <ca:CacheKey>ABC-1234</ca:CacheKey> <ca:Expires>2001-03-09T08:00:00Z</ca:Expires> </ca:CacheControl> </env:Header> <env:Body> <c:CatalogPriceResponse xmlns:c http://example.org/2001/ 06/catalog > <c:PartNumber>ABC-1234</c:PartNumber> <c:PartPrice c:currency USD >120.37</c:PartPrice> </c:CatalogPriceResponse> </env:Body> </env:Envelope> Figure 14.7 SOAP response emitted from catalog SOAP processor [24]. [Copyright # 2002 W3C (MIT, INRIA, Keio University), all rights reserved.]

As we discussed in 2, the lengths to which Web designers have gone to format their pages the way they wanted was quite absurd. HTML had a limited tag set, meaning everything you may have wanted to do to your text wasn t necessarily possible. Yet visitors expectations for graphically appealing pages were still high. Web designers, before HTML 4 came along, had to get creative. One devious device to force indentation of paragraphs or a set amount of white space between paragraphs was the clear GIF. A GIF is an image file (called somename.GIF). It is possible (in a software package designed for image creation, such as Photoshop) to create a 1 1-pixel image. A pixel is the smallest unit of measurement on the screen. Then with HTML, the image can be stretched to fill the desired space. The evil tag would look something like this: <img src= clear.GIF width= 18 height= 1 > Then the Web designer would write the text for the paragraph. The result would be 18 pixels of white space (or about 1 4 inch on most monitors) before the first word of that paragraph. You can imagine what this did to the readability of the HTML when all that junk went at the beginning of the paragraph!

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The parameters of the algorithm [19] were as follows: fuzzi er b 2, termination tolerance e 10 6 , predetermined cluster volumes rj 1, j 1; 2; . . . ; m, maximal

where the squared prediction errors are summed over all the output nodes and over all the records in the training set. The problem is therefore to construct a set of model weights that will minimize this SSE. In this way, the weights are analogous to the parameters of a regression model. The true values for the weights that will minimize SSE are unknown, and our task is to estimate them given the data. However, due to the nonlinear nature of the sigmoid functions permeating the network, there exists no closed-form solution for minimizing SSE, as there exists for least-squares regression. Most neural network models therefore use back-propagation, a gradient-descent optimization method, to help nd the set of weights that will minimize SSE. Back-propagation takes the

between items is re ected in length of the paths between the terminal nodes (Buneman, 1971; Corter, 1996; Dobson, 1974) An additive tree is governed by the additive inequality, which states that if dxy is the path length between x and y, then dij + dkl max(dik + djl, dil + djk) for all i, j, k, l The ultrametric tree is therefore a special case of the additive tree, where the leaf nodes are all equally distant from the root node In an additive tree, this restriction does not hold For example, in the tree shown in panel B of Figure 71, dab = 15, dac = 20, and the dbc = 25, whereas in panel A the dac = dbc In all cases just discussed, only the leaves are explicitly labeled.

The researcher may often label the internal nodes on an ad hoc basis to assist in the readability and interpretation of the clusters The reader, however, should be warned that in such cases the internal labels are arbitrary and not de ned by the clustering algorithm Classi cation Data as Tree Models Corter (1996) argued for the acknowledgment of clustering and trees as models of proximity relationships, rather than as the result of an algorithm for tting data The distinction here is subtle but important Cluster analysis can begin with the notion of some existing underlying clusters The clusters might be subject to noise and error and vary in dispersion and overlap The clusters are sampled with measurements taken on a variety of attributes, which are then subjected to a cluster analysis to recover the true clusters.

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