Key Data Mining Features
SQL server data mining provides the following features in support of integrated data mining solutions:
Multiple data sources
You can use any tabular data source for data mining, including spreadsheets and text files. You can also easily mine OLAP cubes created in Analysis Services. However, you cannot use data from an in-memory database.
Integrated data cleansing, data management, and reporting
Integration Services provides tools for profiling and cleansing data. You can build ETL processes for cleaning data in preparation for modeling, and SSIS conversion also makes it easy to retrain and update models.
Multiple customizable algorithms
In addition to providing algorithms such as clustering, neural networks, and decisions trees, SQL Server Data Mining supports development of your own custom plug-in algorithms.
Model testing infrastructure
Test your models and data sets using important statistical tools as cross-validation, classification matrices, lift charts, and scatter plots. Easily create and manage testing and training sets.
Querying and drillthrough
SQL Server Data Mining provides the DMX language for integrating prediction queries into applications. You can also retrieve detailed statistics and patterns from the models, and drill through to case data.
Client tools
In addition to the development and design studios provided by SQL Server, you can use the Data Mining Add-ins for Excel to create, query, and browse models. Or, create custom clients, including Web services.
Scripting language support and managed API
All data mining objects are fully programmable. Scripting is possible through MDX, XMLA, or the PowerShell extensions for Analysis Services. Use the Data Mining Extensions (DMX) language for fast querying and scripting.
Security and deployment
Provides role-based security through Analysis Services, including separate permissions for drillthrough to model and structure data. Easy deployment of models to other servers, so that users can access the patterns or perform predictions
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Key Data Mining Concepts
Achieving the best results from data mining requires an array of tools and techniques. Some of the most commonly-used functions include:
Data cleansing and preparation
A step in which data is transformed into a form suitable for further analysis and processing, such as identifying and removing errors and missing data.
Artificial intelligence (AI)
These systems perform analytical activities associated with human intelligence such as planning, learning, reasoning, and problem solving.
Association rule learning
These tools, also known as market basket analysis, search for relationships among variables in a dataset, such as determining which products are typically purchased together.
Clustering
A process of partitioning a dataset into a set of meaningful sub-classes, called clusters, to help users understand the natural grouping or structure in the data.
Classification
This technique assigns items in a dataset to target categories or classes with the goal of accurately predicting the target class for each case in the data.
Data analytics
The process of evaluating digital information into useful business intelligence.
Data warehousing
A large collection of business data used to help an organization make decisions. It is the foundational component of most large-scale data mining efforts.
Machine learning
A computer programming technique that uses statistical probabilities to give computers the ability to “learn” without being explicitly programmed.
Regression
A technique used to predict a range of numeric values, such as sales, temperatures, or stock prices, based on a particular data set.
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