1. Promising “Newer” Technologies to Cope with the
Information Flood
Knowledge Discovery and Data Mining (KDD)
Agent-based Technologies
Ontologies and Knowledge Brokering
Non-traditional data analysis techniques
Model Generation
As an Example
To Explain /
Discuss Technologies
As I mentioned in the introduction the goal of this talk is to introduce and describe newer technologies that in my opinion show some promise to cope with the information flood in health care. In this talk, I will focus on 3 particular technologies, namely, ...
Moreover, during the course of the talk I will not only introduce the technologies but also analyze how they can fertilize each other’s application.

2. Why Do We Need so many Data Mining / Analysis Techniques?
No generally good technique exists.
Different methods make different assumptions with respect to the data set to be analyzed
Cross fertilization between different methods is desirable and frequently helpful in obtaining a deeper understanding of the analyzed dataset.

3. Data Mining and Business Intelligence
Increasing potential
to support
business decisions
End User
Making
Decisions
Data Presentation
Business
Analyst
Visualization Techniques
Data Mining
Data
Analyst
Information Discovery
Data Exploration
Statistical Analysis, Querying and Reporting
Data Warehouses / Data Marts
OLAP, MDA
DBA
Data Sources
Paper, Files, Information Providers, Database Systems, OLTP

4. Example: Decision Tree Approach

5. Decision Tree Approach2

6. Decision Trees Example:
Conducted survey to see what customers were interested in new model car
Want to select customers for advertising campaign
training
set

7. One Possibility age<30 city=sf car=van likely unlikely likely

8. Another Possibility car=taurus city=sf age<45 likely unlikely

9. Summary KDD
KDD: discovering interesting patterns from large amounts of data
A natural evolution of database technology, in great demand, with wide applications
A KDD process includes data cleaning, data integration, data selection, transformation, data mining, pattern evaluation, and knowledge presentation
Mining can be performed in a variety of information repositories
Data mining functionalities: characterization, discrimination, association, classification, clustering, outlier and trend analysis, etc.
Multi-disciplinary activity
Important Issues: KDD-methodologies and user-interactions, scalability, tool use and tool integration, preprocessing, interpretation of results, finding good parameter settings when running data mining tools,…

10. Where to Find References?
Data mining and KDD (SIGKDD member CDROM):
Conference proceedings: KDD, and others, such as PKDD, PAKDD, etc.
Journal: Data Mining and Knowledge Discovery
Database field (SIGMOD member CD ROM):
Conference proceedings: ACM-SIGMOD, ACM-PODS, VLDB, ICDE, EDBT, DASFAA
Journals: ACM-TODS, J. ACM, IEEE-TKDE, JIIS, etc.
AI and Machine Learning:
Conference proceedings: Machine learning, AAAI, IJCAI, etc.
Journals: Machine Learning, Artificial Intelligence, etc.
Statistics:
Conference proceedings: Joint Stat. Meeting, etc.
Journals: Annals of statistics, etc.
Visualization:
Conference proceedings: CHI, etc.
Journals: IEEE Trans. visualization and computer graphics, etc.