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In Query Studio, you can sort the columns in a query alphabetically or numerically You can select the column to be sorted and then click the Sort button on the standard toolbar Query Studio sorts the column in ascending order the first time you click the Sort button, in descending order the second time, and removes the sort the third time you click the Sort button Another way to sort data provides you with more specific control over how the data is sorted and is described here Here s how to sort data: 1 From the Edit Data menu, select Sort The Sort pane displays at the bottom of the window:

Data mining addresses the problem of how to analyze the likelihood of observed outcomes when there are too many variables to easily understand the driving factors Typically, organizations build and deploy mathematical models based on the algorithm that best predicts when such very good or very bad outcomes are likely to occur Example applications might include fraud detection, churn analysis, cross-sell effectiveness, threat detection, and a host of others The designers of the models are usually among the most advanced statisticians and analysts within an organization One emerging trend is the embedding of such data mining models into applications to make the sophistication of them transparent and available to a much wider group of business analysts A variety of data mining algorithms are provided in the Oracle Data Mining Option to address different business problems, since predicting their outcomes will likely align with a specific algorithm For example, clustering algorithms are ideal when segmenting groups; classification algorithms are commonly used to predict response; regression is used when estimating values; feature extraction is used to create new but fewer attributes that represent the same information; attribute importance identifies key predictors of outcomes; associations are used to find groups of items with affinity to each other; and anomaly detection identifies rare situations and events Table 7-2 indicates the specific data mining algorithms available in the Oracle database and when these data mining techniques are required

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Part III:

The developer needs a way to identify arguments (information) so that it can be referenced in the event handler This is done by assigning a name to the argument and by identifying the kind of augment within the parentheses of the event handler Think of this as the form that the 911 operator fills in when an emergency call is received The form has Caller, Address, and Nature of Emergency as labels The 911 operator writes down the name of the caller, the address of the emergency, and the nature of the emergency alongside the corresponding label While the emergency call is being processed, the 911 operator and other emergency personnel refer to the labels (Caller, Address, Nature of Emergency) whenever this specific information is required For example, the paramedic might radio the 911 operator, "We're at the address speaking to the caller" The 911 operator and the paramedics refer to the form for the specific address and the caller's name The argument name is like the label on the form The ASPNET engine "writes" the specific information to each label Developers call this assigning a value to the argument You'll learn more about this in the next chapter The Page-Load event handler has two arguments The first argument is called sender and is a kind of Object You'll recall that an Object is an instance of a class This identifies the source of the event The second argument is called e and is a kind of EventArgs, which is information about the event You'll learn more about this later in the book

Our goal for this book is to help you and your baby learn to sign so that, like hundreds of thousands of other families, you can enjoy the many benefits we ve described Just as Sophia, Jennifer, and Bryce have done, your baby can easily learn simple signs for objects, events, feelings, and needs With these signs literally at your baby s fingertips, communication between you can flourish during

Clustering Classification Regression Feature Extraction Attribute Importance Association Anomaly Detection TABLE 7-2

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