Java Data Mining: Strategy, Standard, And Practice: A Practical Guide For Architecture, Design, And Implementation - Mark F. Hornick

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Résumé :
Whether you are a software developer, systems architect, data analyst, or business analyst, if you want to take advantage of data mining in the development of advanced analytic applications, Java Data Mining, JDM, the new standard now implemented in core DBMS and data mining/analysis software, is a key solution component. This book is the essential guide to the usage of the JDM standard interface, written by contributors to the JDM standard....

Biographie:
Mark Hornick has lead the Java Data Mining (JSR-73) expert group since its inception in July of 2000, and now leads the JSR-247 expert group working towards JDM 2.0. Mr. Hornick brings nearly 20 years experience in the design and implementation of advanced distributed systems, including in-database data mining, distributed object management, and Java APIs. Mr. Hornick is a senior manager in Oracle's Data Mining Technologies group.

Mr. Hornick joined Oracle through Oracle's acquisition of Thinking Machines Corporation in 1999. Prior to Thinking Machines, where he served as architect for TMC's next generation data mining software, Mr. Hornick was a Principal Investigator at GTE Laboratories, involved in advanced telecommunications network management software, distributed transaction management research, and distributed object management research.

Mr. Hornick has contributed to several other data mining standards, including the Data Mining Group's PMML, ISO SQL/MM for Data Mining, and the Object Management Group's Common Warehouse Metadata. He has given talks at the International Conference on Knowledge Discovery and Databases, JavaOne, JavaPro Live!, and The ServerSide Symposium on data mining standards and JDM. He has also published various papers and articles over his career.

Mr. Hornick holds a bachelor degree from Rutgers University in Computer Science, and a masters degree from Brown University, also in Computer science where he specialized in distributed object databases....

Sommaire:

Part I - Strategy 1. Overview of Data Mining 2. Solving Problems in Industry 3. Data Mining Process 4. Mining Functions and Algorithms 5. JDM Strategy 6. Getting Started

Part II - Standard 7. Java Data Mining Concepts 8. Design of the JDM API 9. Using the JDM API 10. XML Schema 11. Web Services

Part III - Practice 12. Practical Problem Solving 13. Building Data Mining Tools using JDM 14. Getting Started with JDM Web Services 15. Impacts on IT Infrastructure 16. Vendor implementations

Part IV. Wrapping Up 17. Evolution of Data Mining Standards 18. Preview of Java Data Mining 2.0 19. Summary

A. Further Reading B. Glossary