Data Mining with SQL Server 2005

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Training: Iterating a set of cases and extracting knowledge, rules, or patterns 3. This is commonly known as scoring.

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The Data Mining Engine allows you to store your algorithm-specific content as part of its corresponding mining model object using persistence interfaces that it makes available to you. This means that you simply write to and read from these streaming interfaces without needing to worry about developing your own transactional storage system.

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You are of course free to define and manage your own in-memory structures that you load from the Analysis Server storage via these interfaces. Memory Management Memory objects—such as strings, variants, and arrays—must be allocated and freed using per-algorithm or per-request service provider interfaces passed to plug-in algorithms by the Data Mining Engine. This allows the Data Mining Engine to efficiently manage memory resources and balance them across multiple requests.

Data Mining tutorial for SQL Server – SQLServerCentral

As a plug-in algorithm developer, this is another complex area that you no longer need to spend development effort on. Error Handling Error reporting is done through the standard COM mechanism of creating and populating errorinfo objects. The Data Mining Engine consumes your errorinfo objects and raises your errors in the same way that it reports errors from other external components. Like native errors, your errors may be logged to the console, administrator-specified server log files, or the Windows event log.

This allows Analysis Server administrators to control which plug-in algorithms can be loaded into the Analysis Server process. Plug-in viewers are Winforms UserControls that implement a simple mining viewer interface. In the Build Query child tab, you can either provide the prediction query or click the Build New Query button to build a prediction query using the GUI.

Next, choose the fields one by one against the respective source in the bottom panel of the New Data Mining Query.

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Structured Scheme of Data Mining Based on SQL Server 2005

Configuring Data Mining Query Task 1. Business Intelligence.

Introduction to Data Mining in SQL Server Analysis Services

Install PowerShell on Windows Server No comments yet Microsoft is moving forward aggressively with its strong platform approach that's gone beyond delivering data mining to the masses by providing enterprise tools that help developers embed data mining in applications and core business processes.

Recently, I talked with the Lead Program Manager for data mining, Zhaohui Tang, and the data mining engine lead, Jamie MacLennan, about data mining in SQL Server and what readers should know that they might not be catching in the mainstream product marketing. The first topic they wanted to cover was text mining, which is profiling and pattern discovery in unstructured data such as written documents or transcripts from customer support calls.

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  8. I got Peter Kim's an engineer on the SQL Server Data Mining development team Text Data Mining tutorial and I had soon imported sample user group posting data, created a dictionary and term vectors, trained and tested my samples, and built data-mining models that were ready to deploy. Kim wrote the tutorial for SQL Server Beta 2; so if you're using a later build a few things are different.

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    After talking about text mining, we discussed how valuable platform integration is for data mining in SQL Server SSIS becomes the primary vehicle for data cleansing and preparation for data mining model creation in addition to training and performing predictions against existing data mining models. In Analysis Services, dimensions, and subsequently OLAP cubes or Unified Dimensional Models UDM , can be created based on data mining models letting you expose interesting patterns and segmentation in your reporting and analysis.

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    Data Mining with SQL Server 2005

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