This section provides an overview of the fundamental concepts behind data mining within SQL Server Analysis Services (SSAS). Understanding these concepts is crucial for effectively building, deploying, and querying data mining models.
Introduction to Data Mining
Data mining is the process of discovering patterns and relationships in large datasets. SQL Server Analysis Services leverages sophisticated algorithms to extract valuable insights from your data, enabling better decision-making and predictive analysis. Key aspects include understanding your data, selecting appropriate algorithms, building and training models, and evaluating their performance.
Core Data Mining Concepts
Mining Structures
A mining structure is the foundation of a data mining model in SSAS. It defines the data sources, the columns to be included, their usage (input, predictable, case key, sequence key), and the data transformations applied. It also contains one or more mining models.
- Content: Refers to the type of data associated with a column (e.g., numeric, categorical, text).
- Discretization: The process of converting continuous numeric data into discrete intervals or bins.
- Feature Selection: Techniques used to identify the most relevant attributes for modeling.
Mining Models
A mining model is created based on a mining structure and uses a specific algorithm to discover patterns. SSAS supports a variety of algorithms, each suited for different types of analysis.
- Algorithms: Association Rules, Clustering, Decision Trees, Linear Regression, Logistic Regression, Neural Networks, Sequence Clustering, Time Series, and more.
- Training: The process of feeding data to an algorithm to build a model.
- Prediction: Using a trained model to predict outcomes for new data.
Data Types and Usage
Understanding how SSAS handles different data types and their intended usage is critical for model accuracy:
- Controllable: Attributes that can be manipulated to influence an outcome.
- Predictable: Attributes that the model aims to forecast.
- Key: Attributes that uniquely identify a case or a sequence.
- Ignored: Attributes that are not used in the mining process.
Model Evaluation
After a model is trained, its performance must be evaluated to ensure its reliability and accuracy. SSAS provides various metrics and tools for this purpose.
- Accuracy: How well the model predicts correct outcomes.
- Precision and Recall: Measures used primarily for classification and clustering tasks.
- Lift Charts: Visualize the improvement offered by a model over random selection.
Common Data Mining Applications
- Customer Segmentation: Grouping customers based on their behavior and characteristics.
- Market Basket Analysis: Identifying products that are frequently purchased together.
- Predictive Maintenance: Forecasting potential equipment failures.
- Fraud Detection: Identifying anomalous transactions that may indicate fraud.
- Sales Forecasting: Predicting future sales based on historical data.