Promising “Newer” Technologies to Cope with the

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Presentation transcript:

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.

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.

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

Example: Decision Tree Approach

Decision Tree Approach2

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

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

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

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,…

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.