Unleash the Power of SSAS Data Mining
Table of Contents
- Introduction
- Setting up the Demo
- Understanding the Scenario
- Predicting the Number of Children
- Automatic Reports with Analysis Services Data Mining
- Creating the Data Mining Model
- Publishing and Viewing Reports
- Evaluating Predictive Capabilities
- Adding Additional Data Mining Models
- Conclusion
Introduction
In this article, we will explore Microsoft's data mining capabilities for SQL Server Analysis Services. This overview will provide a comprehensive understanding of how data mining works and its practical applications. We'll start with setting up the demo environment and understanding the scenario. Then, we'll dive into predicting the number of children based on various factors and explore the automatic reporting features of analysis services data mining. We'll also learn how to create data mining models, publish reports, and evaluate predictive capabilities. So, let's get started!
Setting up the Demo
Before we delve into the details, we need to set up a demo environment similar to Microsoft's Tutorial one. This demo revolves around using AdventureWorks Data Warehouse 2012, so make sure to attach the MDF and LDF files. Additionally, we'll utilize a script file that summarizes the scenario. Once everything is set up, we can proceed further.
Understanding the Scenario
The scenario we'll be working with involves a list of customers who have purchased products from our database. Based on data analysis and reports, we have observed that customers without children are more likely to buy our expensive bicycles. This information is crucial as we are planning to open a new store and want to target customers without children for Advertising purposes. However, the mailing list we obtained does not include the number of children. Hence, we need to predict the number of children based on other factors. This predicament sets the tone for our data mining exploration.
Predicting the Number of Children
Predicting the number of children can be challenging, considering the various factors that may influence it, such as car ownership, marital status, or homeownership. In our scenario, we'll assume a correlation between the number of children and other attributes, although it may not hold true in reality. To demonstrate data mining, we will use the existing customer data and a sample of potential customers. By looking at the demographics of these customers, we can identify Patterns and make predictions regarding the number of children.
Automatic Reports with Analysis Services Data Mining
Analysis Services Data Mining provides a powerful feature: automatic reporting. Without the need for extensive coding, we can obtain insightful reports by utilizing the built-in wizard and selecting the desired data and algorithms. These reports offer valuable information about the patterns and correlations within our data. Though not overly fancy, these reports provide a simple yet effective way to extract Meaningful insights.
Creating the Data Mining Model
To create a data mining model, we need to go through the process of configuring the data source, defining the key columns, and selecting the input attributes. The choice of algorithms depends on the type of analysis we wish to perform, such as regression or clustering. While the wizard simplifies the process, it is essential to choose the most appropriate algorithm. By understanding the nature of our data and requirements, we can make informed decisions when creating the data mining model.
Publishing and Viewing Reports
Once we have created the data mining model, we can publish it to the analysis server. This enables us to view the reports generated by the model. These reports provide insights into the relative importance of different attributes and allow for easy comparison and analysis. With the drill-through functionality, we can explore individual records and gain a deeper understanding of the underlying data. While the reporting capabilities may not be groundbreaking, they are a useful tool for understanding the makeup of our data.
Evaluating Predictive Capabilities
Data mining also offers predictive capabilities, but the accuracy of predictions depends on the quality of the data and the algorithms used. It is vital to evaluate the performance of our models to ensure reliable predictions. By testing the predictions against known data, we can assess the effectiveness of our models. In this process, we can compare different algorithms and iteratively improve our predictive capabilities.
Adding Additional Data Mining Models
As we gain more experience and if required, we can expand our data mining capabilities by adding additional models. By incorporating different algorithms, such as Microsoft Clusterer, we can explore new perspectives and enhance our predictive abilities. It is crucial to choose the most appropriate models based on our specific needs and continually explore ways to optimize our data mining efforts.
Conclusion
In conclusion, data mining with Microsoft's SQL Server Analysis Services provides valuable insights into complex datasets. Through the automatic reporting features and predictive capabilities, we can uncover patterns, correlations, and make informed decisions. While the tutorial and documentation may not be up-to-date, the functionality described in this article applies to SQL Server 2016. By experimenting with data mining techniques, we can add a powerful tool to our business intelligence arsenal.
Highlights
- Discover the power of Microsoft's data mining capabilities
- Predict the number of children based on various factors
- Explore automatic reporting with Analysis Services Data Mining
- Create a data mining model and publish reports
- Evaluate the predictive capabilities of your models
- Enhance your data mining efforts with additional models
FAQ
Q: Can data mining accurately predict the number of children?
A: The accuracy of data mining predictions depends on the quality of the data and the algorithms used. It is important to evaluate and validate the predictions against known data.
Q: Are the automatic reports generated by Analysis Services Data Mining customizable?
A: The reports generated by Analysis Services Data Mining are not highly customizable. However, they provide valuable insights into the patterns and correlations within the data.
Q: Can additional data mining models be added to improve predictive capabilities?
A: Yes, additional data mining models can be added to enhance predictive capabilities. By incorporating different algorithms and analyzing data from multiple perspectives, we can improve the accuracy of predictions.