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This affects not just the LDP traffic but the IP traffic as well What we have seen in this example is a mechanism for avoiding the ill effects of a configuration error for the LDP protocol The undesirable consequences of the misconfiguration are avoided and an error can be reported when the condition is detected, thus allowing the operator to identify and rectify the problem 12422 Common misconfigurations for VPN scenarios Assume a network with two VPN customers, A and B, using the same private address space Two new sites are added on the same PE: CE1 in VPN A and CE2 in VPN B Two common misconfigurations are possible: 1 Assigning the customer interface to the incorrect VPN.

Text mining takes ideas from the data mining of highly structured, mostly numeric databases or data warehouses into the world of semistructured or unstructured electronic documents such as email, memoranda, articles, or HTML and XML les Humans have an ability to read and interpret the text documents that has been dif cult to automate with machine intelligence Computers do not easily overcome textual barriers such as slang, synonyms, spelling variations, and contextual meaning However, advances in information retrieval, natural language processing, and corpus-based computational linguistics have enabled text mining technologies to extract information and track topics; to summarize, categorize, and cluster documents; and to conceptually link similar documents even when the words in the documents may be different There is disagreement on de nition and what quali es as text mining The discipline of information retrieval focuses on nding and retrieving what is already known.

Recall from the introduction to the L3 VPN chapter ( 7) that the decision to which VPN CE-originated traffic belongs is based on the interface over which the traffic arrives If the link.

Some argue that technologies like information extraction that identify and tag, for example, people, places, events, and times from a passage in a document nd what is already in the text and are therefore just advanced information retrieval They argue that, to qualify as text mining, the technology must learn something new about the world that is completely outside the document collection being analyzed (knowledge creation) Others relax that requirement and argue that, although the technology may not nd something completely novel, it must at least nd something unknown such as new patterns or trends (knowledge discovery) But all agree that text mining is a close relative of data mining and you will nd many similarities throughout: from the way the text is processed to the technologies used to process it.

When you zoom a worksheet to 39 percent or lower, you see a border around the named ranges with the name displayed in blue letters, as shown in Figure 3-12. The border and name do not print; they simply help you visualize the named ranges on your sheet.

To build on what has been covered so far and to provide an appreciation for processing text, the following will focus on the clustering technology The general architecture of a text mining system is initially discussed The general architecture shown in Fig 523 should feel familiar In place of a data set you nd a document collection; in place of an observation you nd a.