The document provides an overview of data mining, defining it as the process of discovering patterns in large datasets using techniques from artificial intelligence, machine learning, statistics, and database systems. It outlines the data mining process following the CRISP-DM framework, which includes phases such as business understanding, data preparation, and modeling, while also detailing specific data mining algorithms including decision trees, clustering, time series, and association rules. Additionally, it covers the creation and management of mining structures and models within SQL Server Analysis Services (SSAS).

1. Data Mining
In SSAS
Ram Kedem

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What is Data Mining ?
• the computational process of discovering patterns in large
data sets involving methods at the intersection of artificial
intelligence, machine learning, statistics, and database
systems.
• The overall goal of the data mining process is to extract
information from a data set and transform it into an
understandable structure for further use.

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Data Mining Process
• Cross-Industry Standard Process for Data Mining (CRISP-DM)
consists of six cyclic phases :
• Business Understanding
• Data Understanding
• Data Preparation
• Modeling
• Evaluation
• Deployment

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Data Mining Algorithms
Covered In This Lesson
• Decision Trees
• Clustering
• Time Series
• Association rules

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Decision Trees
• Decision Trees
• a tree-like graph or model of decisions and their possible
consequences, including chance event outcomes, resource costs
• Very common, used for classification.

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Clustering
• Clustering
• grouping a set of objects in such a way that objects in the same
group (called a cluster) are more similar (in some sense or
another) to each other than to those in other groups (clusters)
• Widely used in areas of fraud detection

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Data Validation Sample
• http://www.sqlserverdatamining.com/ssdm/

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Data Validation Sample
• http://www.sqlserverdatamining.com/DataValidation/default.
aspx

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Time Series
• Time Series
• A time series is a collection of observations made chronologically,
Used to predict a number of some sort.

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Association rules
• Association rules
• Association rules are if/then statements that help uncover
relationships between seemingly unrelated data in a relational
database or other information repository.

11. Create New Mining
Structure

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Define your Data Source

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Define your DSV

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Create new mining structure
• Right click on Mining Structures and choose New Mining
Structure

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Select the Definition Method

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Create the Mining Structure

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Select Data Source View

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Specify Table Types

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Specify Training Data

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Suggest Related Columns

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Columns Content & Data Types

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Create Testing Set

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Completing the Wizard

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View Mining Model
• Deploy and Process the Mining Model.
• View the Mining Model in Mining Models Tab

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Adding Mining Models
• It is possible to add more mining models by right-clicking at
the Mining models area and choosing New Mining Model.
• Deploy and Process once finished.

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Mining Model Viewer

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Dependency Network

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Mining Accuracy Chart - Input

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Lift Chart

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Profit Chart

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Profit Chart

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Mining Model Prediction

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Mining Model Prediction

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Query Results View

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Order the Data Differently

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Singleton Query

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Singleton Query

38. Time Series Mining
Structure

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Time Series Mining Structure
• Right click on Mining Structures and choose New Mining
Structure

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Select Definition Method

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Create the Data Mining
Structure

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Select DSV

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Specify Table Types

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Specify Training Data

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Specify Columns Content and
Data Types

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Complete the Wizard

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Mining Model Viewer