Business Applications of Data Mining

By: Team 1

Data Mining – An Introduction

Discovery New Information Patterns Rules

Extensive Data

Data Mining – Goals

Prediction e.g. What consumers will buy under certain discounts

Identification e.g. Gene identified by certain DNA sequence

Classification e.g. Health foods, party foods, school lunch foods

Optimization e.g. Maximizing sales and profit

Data Mining – Types of Info.

Association Rules When shopper buys A, likely to buy B

Classification Hierarchies Mutual funds judged by growth, income, stability

Sequential Patterns Association over time

Types of Info, Continued

Patterns within Time Series Two products sell same in summer, not in winter

Clustering Medication grouped together based on side effects

Marketing

Theory for Data Mining in Marketing

Benefits in Marketing

Segmentation

Marketing

Optimized Segmentation

Client Applications

MCI’s Friends and Family

Data Mining in Health Care

Why use Data Mining? What does it do? Who will use it? Who will benefit? Examples of real world applications

Why use Data Mining?

Demand for quality health care is rising Need more affordable health care Consolidation of industry = more data Data Mining Tools are here NOW

What does it do?

Discovers patterns Finds hidden correlation Gives the user information needed to

make the best decision

Who will use it?

Hospitals Clinics Outpatient Centers Pharmacies It will create a company standard, which

will then create an industry standard

Who will benefit?

Chief Medical Officer enterprise reporting

Financial Analyst resource consumption in the organization

Physician access to patient health history

Patients faster, safer, cheaper, health care

Pneumonia Deaths

Death rate was 12% Average stay two weeks Discovered problem between doctors

and the lab Death rate now 9% Average stay is 5 days

Detecting Insurance Fraud

Devise rules, example: Ambulance trip with no medical services

Help investigators use time more efficiently

Health Care Wrap-Up

New knowledge will not be discovered by the program, only by the user

Industry will save money and become more efficient

Manufacturing

Key Applications: Design and Analysis of Experiments

Reliability Analysis and Life Expectancy

Field Failure Analysis and Reporting

Supply Chain Optimization

Demand Forecasting, Optimization and Reporting

Statistical Process Control/Six Sigma INSIGHTFUL MANUFACTURING SOLUTIONShttp://www.insightful.com/industry/manufacturing/default.asp

Manufacturing

CRISP-DM- Real world practical

input as base for creation

CRISP process:- Business Understanding - Data Understanding - Data Preparation - Modeling- Evaluation - Deployment

Manufacturing

HP and Motorola chips- Semiconductor yield enhancement: 10x faster than standard approaches, yield increases ranged from 3% to 15% - Manufacturing optimization

Tin platted Steel- 90% of product used for food shipping

- Excess coating reduced 30%, savings $.6Mil

- 95% of cases, error of less than 10% in prediction

- Actual results: 99.7% of the cases showing good performance

Manufacturing

CRISP-DM HP chips Motorola chips

- Semiconductor yield enhancement

- Manufacturing optimization

Tin platted Steel

Manufacturing

Future: CALD research- Autonomous Decision-Making Systems

* Autonomy (get rid of experiment-design-trained statistician). * Minimizing the number of expensive experiments. * Optimizing the expected value, given very noisy evaluations.

3M and a large U.S. food processing company experience financial savings

Works Cited Abajo, Nicols, et al. "ANN Quality Diagnostic Models for Packaging

Manufacturing: An Industrial Data Mining Case Study." KDD '04: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Seattle, WA, USA, <http://doi.acm.org/10.1145/1014052.1016917>.

Apte, Chidanand, et al. "Business Applications of Data Mining." Communications of the ACM 45.8 (2002): 49-53. <http://doi.acm.org/10.1145/545151.545178>.

Course Text Hirji, Karim K. "Exploring Data Mining Implementation." Communications

of the ACM 44.7 (2001): 87-93. <http://doi.acm.org/10.1145/379300.379323>.

Silver, Michael, et al. "Case Study: How to Apply Data Mining Techniques in a Healthcare Data Warehouse." Journal of Healthcare Information

Management 15.2 (2001): 155-64.