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Promising “Newer” Technologies to Cope with the

Promising “Newer” Technologies to Cope with the. Knowledge Discovery and Data Mining (KDD) Agent-based Technologies Ontologies and Knowledge Brokering Non-traditional data analysis techniques. Information Flood. Model Generation As an Example To Explain / Discuss Technologies.

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Promising “Newer” Technologies to Cope with the

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  1. Promising “Newer” Technologies to Cope with the • Knowledge Discovery and Data Mining (KDD) • Agent-based Technologies • Ontologies and Knowledge Brokering • Non-traditional data analysis techniques Information Flood Model Generation As an Example To Explain / Discuss Technologies

  2. Why Do We Need so manyData 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 Business Analyst Data Presentation 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 Y N city=sf car=van Y Y N N likely unlikely likely unlikely

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

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