- Home
- Data Mining Tutorial
- Source Data Description - Data Mining
Source Data Description - Data Mining
Interview Questions
Practice Test
Pragnyameter Exam
All Tutorials
Job SearchFigure gives the partial schema of the MoviePick.com data mart. Two tables are shown in the figure: Customers and Purchase. The Customers table contains the customer demographic information such as age, education, gender, income, and so on. It is a dimension table. The Purchase table stores the historical transactions of the customer’s previous purchases. It is a fact table with two columns: CustomerID and Movie. Each customer has bought a set of movies. The relationship between these two tables is 1 to n.
|
|
Data Mining Related Tutorials |
|---|
| R Programming language Tutorial |
Data Mining Related Interview Questions |
|
|---|---|
| Data Mining Interview Questions | Data Center Management Interview Questions |
| Clinical SAS Interview Questions | Machine learning Interview Questions |
| Data analyst Interview Questions | R Programming language Interview Questions |
| Advanced SAS Interview Questions | Data Center Technician Interview Questions |
| Data Analysis Expressions (DAX) Interview Questions | Predictive Modeling Interview Questions |
Data Mining Related Practice Tests |
|
|---|---|
| Data Mining Practice Tests | Data Center Management Practice Tests |
| Clinical SAS Practice Tests |
Data Mining Tutorial
- What Is Data Mining
- Business Problems For Data Mining
- Data Mining Tasks
- Data Mining Techniques
- Data Flow
- Data Mining Project Cycle
- Data Mining And The Current Market
- Current Issues And Challenges
- Data Mining Standards
- Ole Db For Dm And Xml For Analysis
- New Trends In Data Mining
- Introducing Ole Db
- Why Ole Db For Data Mining?
- Exploring The Basic Concepts In Ole Db For Data Mining
- Dmx
- Understanding Schema Rowsets
- Understanding Extensions For Mining Structures
- Introducing The Business Intelligence Development Studio
- Setting Up Your Data Sources
- Creating And Editing Models
- Processing
- Using Your Models
- Using Sql Server Management Studio
- Introducing The Naive Bayes Algorithm
- Understanding Naive Bayes Principles
- Naive Bayes Parameters
- Naïve Bayes Algorithm
- Introducing Decision Trees
- Decision Tree Principles
- Decision Tree Algorithm Parameters.
- Using Decision Trees
- Introducing The Microsoft Time Series Algorithm
- Introducing The Principles Of The Microsoft Time Series Algorithm
- Understanding The Algorithm Parameters
- Using Microsoft Time Series
- Introducing The Microsoft Clustering Algorithm
- Introducing The Principles Of Clustering
- How To Tune The Behavior Of The Clustering Algorithm?
- Using Clustering Models
- Introducing The Microsoft Sequence Clustering Algorithm
- Microsoft Sequence Clustering Algorithm Principles
- Clustering Algorithm
- Using The Sequence Clustering Algorithm
- Introducing Microsoft Association Rules
- Association Algorithm Principles
- Algorithm Parameters Settings
- Using The Association Algorithm
- Introducing The Principles Of The Microsoft Neural Network
Algorithm - Introducing The Algorithm Parameters
- Introducing Olap
- Performing Calculations In Data Mining
- Browsing A Cube
- Understanding Unified Dimension Modeling
- Understanding The Relationship Between Olap And Data Mining
- Building Olap Mining Models Using Wizards And Editors
- Understanding Data Mining Dimensions
- Using Mdx Inside Dmx Queries
- Using Analysis Management Objects For The Olap Mining Model
- Introducing Ssis
- Data Mining In Ssis Environment
- Introducing Analysis Services Architecture
- Xml For Analysis
- Processing Architecture
- Data Mining Administration
- Data Mining Apis
- Using Analysis Services Apis
- Using Microsoft.analysisservices To Create And Manage Mining Models
- Browsing And Querying Mining Models
- Source Data Description
- Building Your Model
- Making Predictions
- Integrating Predictions With Web Applications
- Configuring Analysis Services For Session Models
- Using The Advanced Forecasting Tool
- Exceltimeseries Add-in Architecture
- Building The Input Data Set
- Creating And Training The Mining Model
- Forecasting The Series
- Bringing It All Together
- Understanding Plug-in Algorithms
- Using Data Mining Viewers
- Recapping The Highlights Of Sql Server 2005 Data Mining
- Exploring New Data Mining Frontiers And Opportunities
Introduction To Data Mining
Ole Db For Data Mining
Using Sql Server Data Mining
Microsoft Naive Bayes
Microsoft Decision Trees
Microsoft Time Series
Microsoft Clustering
Microsoft Sequence Clustering
Microsoft Association Rules
Microsoft Neural Network
Mining Olap Cubes
Data Mining With Sql Server Integration Services
Sql Server Data Mining Architecture
Programming Sql Server Data Mining
Implementing A Web Cross-selling Application
Advanced Forecasting Using Microsoft Excel
Extending Sql Server Data Mining
Conclusion And Additional Resources
