Mastering SQL Server 2014 Data Mining by Amarpreet Singh Bassan, Visit Amazon's Debarchan Sarkar

By Amarpreet Singh Bassan, Visit Amazon's Debarchan Sarkar Page, search results, Learn about Author Central, Debarchan Sarkar,

Master deciding on, utilizing, and deploying facts mining versions to construct robust predictive research frameworks

About This Book

  • Understand the several levels of information mining, besides the instruments used at every one stage
  • Explore the several information mining algorithms in depth
  • Become a professional in optimizing algorithms and situation-based modeling

Who This publication Is For

If you're a developer who's engaged on information mining for big businesses and want to increase your wisdom of SQL Server information Mining Suite, this ebook is for you. no matter if you're fresh to info mining or are a pro professional, it is possible for you to to grasp the talents had to construct an information mining solution.

What you are going to Learn

  • Get an summary of the information mining lifestyles cycle
  • Understand the intricacies of SQL Server BI Suite with the aid of a pragmatic example
  • Collate facts from assorted facts assets and construct an information warehouse
  • Gain in-depth wisdom concerning the quite a few information mining versions corresponding to type, segmentation, organization, and more
  • Perform info mining utilizing sizeable info and Excel add-ins
  • Work on real-world info and achieve insights into it utilizing quite a few information mining algorithms
  • Fine song information mining models
  • Troubleshoot difficulties encountered in the course of facts mining actions played during this book

In Detail

Whether you're new to info mining or are a pro professional, this booklet provides you with the talents you must effectively create, customise, and paintings with Microsoft info Mining Suite. beginning with the fundamentals, this e-book will hide how you can fresh the knowledge, layout the matter, and select an information mining version that would provide the so much exact prediction.

Next, you may be taken throughout the numerous category types akin to the choice tree information version, neural community version, in addition to Naive Bayes version. Following this, you are going to know about the clustering and organization algorithms, in addition to the sequencing and regression algorithms, and comprehend the information mining expressions linked to every one set of rules. With considerable screenshots that supply a step by step account of the way to construct a knowledge mining answer, this ebook will verify your luck with this state of the art information mining system.

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Extra info for Mastering SQL Server 2014 Data Mining

Example text

At present, data mining is not supported by the tabular model data and therefore we will be using the multidimensional project for our example, shown in the following screenshot: [ 41 ] Data Model Preparation and Deployment We now fill the following details: • Name: TestDataMining • Location: D:\DataMining\TestDataMining (you can choose a drive of your choice) • Solution name: TestDataMining Then, select the checkbox for Create directory for solution and click on OK. We now add a data source to the project by clicking on Data Source and then clicking on New, as shown here: [ 42 ] Chapter 2 The list of available providers that the data source supports is shown here: We click on New and then make the selection, as shown: In the next screen, we select the Use the service account option and name the data source in the next screen as TestDataMining and then click on Finish.

As it is clear, ETL has the following three parts: • Extract: The data is extracted from a different source database and other databases that might contain the information that we seek • Transform: Some transformation is applied to the data to fit the operational needs, such as cleaning, calculation, removing duplicates, reformatting, and so on • Load: The transformed data is loaded into the destination data store database [ 16 ] Chapter 1 We usually believe that the ETL is only required till we load the data onto the data warehouse but this is not true.

We will now get more specific on the meaning of the benchmark data or the set of conditions. The following are some of the situations that would help us to get a deeper understanding of what criteria a benchmark data will possess: • The data will have a very high conformance to the constraints or rules of the data store such as the target data type, the target data ranges, the target foreign key constraints, the target unique constraints, and so on. • The data should not be incomplete, for example, some of the data or group of data should together add up to a fixed total.

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