Custom cover image
Journeys to Data Mining : Experiences from 15 Renowned Researchers / edited by Mohamed Medhat Gaber
Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Series: SpringerLink BücherPublisher: Berlin, Heidelberg : Springer, 2012Description: Online-Ressource (VIII, 241 p. 33 illus, digital)ISBN:- 9783642280474
- 025.04
- QA75.5-76.95
Contents:
Summary: Data mining, an interdisciplinary field combining methods from artificial intelligence, machine learning, statistics and database systems, has grown tremendously over the last 20 years and produced core results for applications like business intelligence, spatio-temporal data analysis, bioinformatics, and stream data processing. The fifteen contributors to this volume are successful and well-known data mining scientists and professionals. Although by no means an exhaustive list, all of them have helped the field to gain the reputation and importance it enjoys today, through the many valuable contributions they have made. Mohamed Medhat Gaber has asked them (and many others) to write down their journeys through the data mining field, trying to answer the following questions: 1. What are your motives for conducting research in the data mining field?2. Describe the milestones of your research in this field. 3. What are your notable success stories?4. How did you learn from your failures?5. Have you encountered unexpected results?6. What are the current research issues and challenges in your area?7. Describe your research tools and techniques. 8. How would you advise a young researcher to make an impact?9. What do you predict for the next two years in your area?10. What are your expectations in the long term?In order to maintain the informal character of their contributions, they were given complete freedom as to how to organize their answers. This narrative presentation style provides PhD students and novices who are eager to find their way to successful research in data mining with valuable insights into career planning. In addition, everyone else interested in the history of computer science may be surprised about the stunning successes and possible failures computer science careers (still) have to offerSummary: Data mining, an interdisciplinary field combining methods from artificial intelligence, machine learning, statistics and database systems, has grown tremendously over the last 20 years and produced core results for applications like business intelligence, spatio-temporal data analysis, bioinformatics, and stream data processing.The fifteen contributors to this volume are successful and well-known data mining scientists and professionals. Although by no means an exhaustive list, all of them have helped the field to gain the reputation and importance it enjoys today, through the many valuable contributions they have made. Mohamed Medhat Gaber has asked them (and many others) to write down their journeys through the data mining field, trying to answer the following questions:1. What are your motives for conducting research in the data mining field?2. Describe the milestones of your research in this field.3. What are your notable success stories?4. How did you learn from your failures?5. Have you encountered unexpected results?6. What are the current research issues and challenges in your area?7. Describe your research tools and techniques.8. How would you advise a young researcher to make an impact?9. What do you predict for the next two years in your area?10. What are your expectations in the long term?In order to maintain the informal character of their contributions, they were given complete freedom as to how to organize their answers. This narrative presentation style provides PhD students and novices who are eager to find their way to successful research in data mining with valuable insights into career planning. In addition, everyone else interested in the history of computer science may be surprised about the stunning successes and possible failures computer science careers (still) have to offer.PPN: PPN: 165163338XPackage identifier: Produktsigel: ZDB-2-SCS
Journeys to Data Mining; Experiences from 15 Renowned Researchers; Contents; List of Contributors; Introduction; 1 Preamble; 2 Dean Abbott; 3 Charu Aggarwal; 4 Michael Berthold; 5 John Elder; 6 Chris Clifton; 7 David Hand; 8 Cheryl Howard; 9 Hillol Kargupta; 10 Dustin Hux; 11 Colleen McCue; 12 Geoff McLachlan; 13 Gregory Piatetsky-Shapiro; 14 Shusaku Tsumoto; 15 Graham Williams; 16 Mohammed J. Zaki; 17 Remarks; Data Mining: A Lifetime Passion; 1 Early Data Mining; 2 Undergraduate Education; 3 Graduate School; 4 An Introduction to Data Mining: Barron Associates, Inc.; 5 The Employee Years
6 The Data Mining Deep Dive7 Going at Data Mining Alone; 8 Visibility as a Data Miner; 9 Lessons Learned and Advice; References; From Combinatorial Optimization to Data Mining; 1 Motivation; 2 Milestones and Success Stories; 2.1 High-Dimensional Data Mining; 2.2 Data Stream Mining; 2.3 Privacy-Preserving Data Mining; 2.4 Other Recent Topics; 3 Research Tools and Techniques; 4 Lessons in Learning from Failures; 5 Making an Impact; 5.1 Educational Impact; 6 Future Insights; 6.1 Short-Term Challenges; 6.1.1 Graph Mining and Social Networks; 6.1.2 Cloud Computing; 6.2 Long-Term Challenges
7 SummaryReferences; From Patterns to Discoveries; 1 The Past; 2 Types of Miners; 2.1 Parameterization; 2.2 Pattern Detection; 2.3 Hypothesis Generation; 3 Tools; 4 Sparking Ideas; 5 Conclusions; References; Discovering Privacy; 1 Motivation; 2 Milestones and Success Stories; 3 Lessons in Learning from Failures; 4 Current Research Issues and Challenges; 5 Research Tools and Techniques; 6 Making an Impact; 7 Future Insights; 7.1 Short Term; 7.2 Long Term; 8 Summary; References; Driving Full Speed, Eyes on the Rear-View Mirror; 1 Bright Lights; 2 Tom Swift and His Amazing Computing Device
3 Robots that Learn4 High Finance and Higher Ed; 5 Bat Ensembles; 6 Striking Out; 7 The Power of Data Mining; 8 The Business of Data Mining; 9 Take or Give?; 10 But Is that All There Is? Is It Just that ``He Who Dies with the Most Toys, Wins?´´; 11 Full Circle; References; Voyages of Discovery; 1 Data Mining and Me; 2 Challenges; 3 Limitations; 4 Current Research Issues and Challenges; 5 Research Tools and Techniques; 6 Making an Impact; 7 The Future; 8 Summary; References; A Field by Any Other Name; 1 What Makes a Data Miner?; 2 The Long and Winding Road Through Academia
3 Into the Real World3.1 Tax Fraud Detection; 3.2 Insider Threat Detection; 4 Looking Ahead; References; An Unusual Journey to Exciting Data Mining Applications; Making Data Analysis Ubiquitous: My Journey Through Academia and Industry; 1 Motivation; 2 Milestones and Success Stories; 2.1 Abstraction of DDM Problems; 2.2 DDM Algorithm Development; 2.2.1 Distributed Data Mining for Synchronous Environments; 2.2.2 Distributed Data Mining in Asynchronous Environments; 2.3 Applications and Commercial Product Development; 2.3.1 Vehicle-Performance Monitoring Products from Agnik; MineFleet® Overview
Onboard Hardware
No physical items for this record