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If your computer contains any data that you want to keep, be sure to back it up now. Even if you have multiple disk partitions, and don t expect to write over the partitions you want, a backup is a good precaution in case something should go wrong.

Let s use this simple example to discuss what diff discovers and displays when it is invoked by Juana and when it compares the original Web site development team list (filename: /gutiejua/admin/RFI_Web.0301) to the new one (filename: /gutiejua/admin/RFI_Web.0801):

Function Equivalents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 704 Converting Database Functions . . . . . . . . . . . . . . . . . . . . . . . . . . 714

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When needing to make sense of a large set of data, the data can be broken down into smaller groups of observations that share something in common Knowing the contents of these smaller groups helps in understanding the entire data set Clustering is a widely used and exible approach to analyzing data, in which observations are automatically organized into groups Those observations within a particular group are more similar to each other than to observations in other groups This approach has been successfully utilized in a variety of scienti c and commercial applications, including medical diagnosis, insurance underwriting, nancial portfolio management, organizing search results, and marketing For example, clustering has been used by retail organizations to analyze customer data based on historical purchases, along with information about the customer, such as their age or where they live.

Customers are grouped using clustering approaches, and speci c marketing campaigns are then formulated based on the identi ed market segments A data set of animals will be used to illustrate clustering Table 31 describes a series of animal observations (taken from http://archiveicsuciedu/ml/datasets/ Zoo; Murphy and Aha, 1994) Each animal is characterized by a number of variables, including several binary variables, such as whether the animal has hair (hair) or produces milk (milk) The data set also includes a count of the number of animal legs (legs) Cluster analysis organizes the data into groups of similar animals Using the numeric variables shown in Table 31, the data set can be clustered in a number of ways Figure 31 shows one possible grouping of this data A description of how this particular set was clustered will be discussed later in the chapter In Fig 3.

1 the animals have been organized into four groups representing the following general categories: (1) mammals, (2) sh and amphibians, (3) invertebrates, and (4) birds Each group represents a series of similar animals, with the mammals group primarily representing a group of four-legged animals that produce milk, the sh and amphibians group primarily representing a group of aquatic animals, the invertebrates group primarily representing a set of six-legged animals with no backbone, and the birds group primarily representing a set of two-legged airborne animals with feathers Based on an initial inspection of the clusters, the grouping may not appear to adequately represent the different types of animals For example, in Fig 31, an.

The vi Editor Table 10.2 Key Sequences for Deleting Text in vi KEY SEQUENCE x dw d$ d0 dd dG u U DELETE ACTION APPLIED To a single character To the end of the current word To the end of the line To the start of the line The entire line To the end of the file Undo the last change Restore the entire line (only if the cursor has not left the line)

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