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DATA MINING.

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Presentation on theme: "DATA MINING."— Presentation transcript:

1 DATA MINING

2 What is Data Mining? process that uses various tools to discover patterns and relationships in data that may be used to make valid predictions.

3 DATA MINING v/s DATA WAREHOUSING
main repository of an organizations historical data. Raw material for DSS. Where as Data Mining Process which is done on the data from data warehouse.

4 Basic Structure

5 Example Pattern discovery of Midwest Grocery revealed.
Men bought diapers on Thursdays and Saturdays, they also tended to buy beer. Weekly grocery shopping on Saturdays. On Thursdays they only bought a few items. Conclusion - they purchased the beer to have it available for the upcoming weekend.

6 Elements of Data Mining
Extract, transform, and load transaction data onto the data warehouse system. Store and manage the data in a database system. Provide data access to business analysts and information technology professionals. Analyze the data by application software. Present the data in a useful format, such as a graph or table.

7 The Basic Steps of Data Mining
1. Define business problem 2. Build data mining database 3. Explore data 4. Prepare data for modeling 5. Build model 6. Evaluate model 7. Deploy model and results

8 Step 1 - Define business problem
Understand data. Identify the problem. Define objective.

9 Step 2 - Build data mining database
Maximum time requiring step. Data to be mined on database. Don’t use corporate database.

10 Step 3 - Explore data Identify Use good interface and fast computer responses.

11 Step 4 - Prepare data for modeling
Final data preparation step a. Select variables b. Select rows c. Construct new variables d. Transform variables

12 Step 5 - Build model Iterative process
Explore various alternatives to come up with the one that suits the business.

13 Step 6 - Evaluate model Feasibility analysis of the model is done.

14 Step 7 - Deploy model and results
Two ways to use the model Recommend actions based on simply viewing the results. Apply the model to different data sets.

15

16 What it cannot do? Its not a magic wand.
It only tells the pattern but not the value. Pattern may not have cause and effect.

17 THANK YOU


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