Data Mining Problems
CIS 501: Information Systems for Managers
Data Mining Problems
Introduction
Problem 1: Data-Based Decision Making
Problem 2: Market Basket Analysis: Association Analysis
Problem 3: Market Basket Analysis: Concept Tree/Sequence Analysis
Problem 4: Decision Tree
Problem 5: Clustering/Nearest Neighbor Classification
Problem 6: Clustering Problem 1: Data-Based Decision Making
Supermarket Product Placement
Suppose that we are responsible for managing product placement within a local supermarket. Our shelving units have 6 shelves each and are numbered from 1 to 6—with 1 being the lowest shelf and proceeding upward until the highest shelf is assigned the number 6. While there are many placement options that we should consider, we decide to look for any correlations between the row a product is placed on and its sales. Since we have our data stored in a data warehouse, it is easily accessible and responds quickly to our data request. Consider each of the following:
· What judgments can you make regarding the placement of each type of product being considered?
Answer - I think that we are more likely to place those items that are in higher demand by customers and those items that the company wants to generate the greatest profit from on the shelves that have the best sales
· What is the consequence of making the wrong choice?
Answer - Profit decreases, inventory doesn’t turn over
· What types of products do you think each of the product groupings represent?
Answer - Most likely to sell/greatest profitability to least likely to sell/lowest profitability
· What target markets can you associate with each product group?
Answer - ?
Problem 2: Market Basket Analysis: Association Analysis
Example 1: Our data mining program has performed association analysis and has generated a listing of items that are typically purchased together. Two sets of items currently have your attention: o Peanut Butter,
Data Mining Problems
Introduction
Problem 1: Data-Based Decision Making
Problem 2: Market Basket Analysis: Association Analysis
Problem 3: Market Basket Analysis: Concept Tree/Sequence Analysis
Problem 4: Decision Tree
Problem 5: Clustering/Nearest Neighbor Classification
Problem 6: Clustering Problem 1: Data-Based Decision Making
Supermarket Product Placement
Suppose that we are responsible for managing product placement within a local supermarket. Our shelving units have 6 shelves each and are numbered from 1 to 6—with 1 being the lowest shelf and proceeding upward until the highest shelf is assigned the number 6. While there are many placement options that we should consider, we decide to look for any correlations between the row a product is placed on and its sales. Since we have our data stored in a data warehouse, it is easily accessible and responds quickly to our data request. Consider each of the following:
· What judgments can you make regarding the placement of each type of product being considered?
Answer - I think that we are more likely to place those items that are in higher demand by customers and those items that the company wants to generate the greatest profit from on the shelves that have the best sales
· What is the consequence of making the wrong choice?
Answer - Profit decreases, inventory doesn’t turn over
· What types of products do you think each of the product groupings represent?
Answer - Most likely to sell/greatest profitability to least likely to sell/lowest profitability
· What target markets can you associate with each product group?
Answer - ?
Problem 2: Market Basket Analysis: Association Analysis
Example 1: Our data mining program has performed association analysis and has generated a listing of items that are typically purchased together. Two sets of items currently have your attention: o Peanut Butter,