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Decision trees are a popular hypothesis language as they are easy to comprehend and give an explicit model of the decision-making process. An internal node of an induced decision tree speci es a test on an attribute of the data set (though more complex trees may be built by specifying tests at nodes based on more than one attribute). Each outgoing branch of the node corresponds to a possible result of the test and leaf nodes represent the class label to be assigned to an instance. To classify an instance, a path from the root node of the decision tree is traced to a leaf node; each internal node reached speci es which outgoing branch should be taken, depending on the value of the relevant attribute in the instance. The instance is assigned the class label of the leaf node reached at the end of the path. Each individual path of the tree may also be equated with an if-then rule. In traditional machine learning, decision trees are induced by following a divide-and-conquer strategy whereby, depending on the splitting criterion used, the training data are partitioned into disjoint subsets and the algorithm is applied recursively to each subset. Examples of such algorithms that induce decision trees are ID3 [279], C4.5 [281], and CART: classi cation and regression trees [44]. Such learning algorithms generally use an attribute-value representation as the example language; that is, each instance in the training data is described by speci c values of a given set of attributes. The domain of each attribute may be nite or nominal, or numerical.

ACL Diagnostics cannot be used on Group Policy objects (GPOs) because GPOs do not have a distinguished name (DN).

$name = lc($name); my @strArray = (); foreach my $str (split(/ /,$name)) {

push(@strArray, ucfirst($str)); } my $formattedName = join( , @strArray);

Clustering is an unsupervised learning problem it deals with nding structure in a collection of unlabeled data. The clustering method itself can be thought of as the process of organizing objects into groups whose members share similar

All in all, Acldiag.exe is easy to use. Figure C-5 shows you the utility, and Table C-3 gives the command-line syntax.

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Here the $name variable is processed The first line uses Perl s built-in lc() function, which lowercases a given string Because lc() is given the $name string as the parameter, it returns a lowercase value of the name, which we store back in the name variable $name We lowercase the name so that we can uppercase only the first letter of each name Now a user named Carol Godsave might enter many different combinations of her name For example:

As you can see the user can enter one of these or many other combinations of letters to represent her name However, we would want to display Carol Godsave since because that s the proper formatting for her name So our processing code block uses the following algorithm: 1 Lower case the name This effectively turns whatever Carol entered for her name into carol godsave 2 Split each word separated by a space character This gives us are carol and godsave 3 Uppercase only the first letter of each part This gives us Carol and Godsave 4 Put together the parts by separating them with a space character This returns Carol Godsave , which is exactly what we want In this code segment we use an array called @strArray, which we initialize to empty the list Then a foreach loop is used to cycle through the each part of the name that is split using the split() function found in Perl The split() is called with two parameters: separator, which in this case is a white space character written in regular expression format / /, and the string that needs to be separated, which in this case is $name The split() function returns the separated parts in an array So, effectively, in Carol s case the foreach loop looks like:

foreach my $str ( carol godsave ) { push(@strArray, ucfirst($str)); }

properties A cluster, then, is a collection of objects that are similar but are dissimilar to objects belonging to other clusters This has been seen, to some extent, in Section 23 where the concept of equivalence classes is similar to the clustering concept Objects belong to an equivalence class if they contain the same attribute values for a given set of attributes Each class can be thought of as a cluster, with the similarity criterion being the strict requirement that objects are identical Two of the main approaches to clustering are partitional and hierarchical Partitional clustering seeks to construct partitions of a dataset of n objects into k clusters The goal of these methods is to optimize a chosen partitioning criterion for a given k In order to nd the global optimal, an exhaustive enumeration of all partitions would have to be carried out.

Figure C-5: Acldiag.exe acldiag ObjectDN [/chkdeleg] [/fixdeleg] [/geteffective:{User | Group}] [/schema] [/skip] [/tdo]

The loop cycles through each part and uppercases only the first character of each part using the built-in ucfirst() function call When ucfirst() is given a string parameter, it uppercases only the first character and returns the modified string For example ucfirst( carol ) will return Carol ; similarly, ucfirst( godsave )

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