Hello, I'm an assistant Professor in the computer science department working on mainly data mining algorithm developent. Thanks to the NSF and Google for supporting my research projects and my wife for these two projects. In my spare time I enjoy history, am an avid movie buff and playing golf.

• New Stuff
• Research
• Personal Interests
• Awards
• Ph.D. Students
  • • Current Students
    • Alumni
• Recent Books
• Tutorials
• Recent and Upcoming Research Activities
• Select Recent Publications by Area
• Courses and Teaching 

New Stuff

We are running a workshop at KDD this year on Data Mining with Constraints:  Workshop Webpage and CFP

New Journal of DMKD Paper on Constraints and Agglomerative clustering: PDF

The Book has been sent to the publishers, check it out! Click here for book details, Buy from Amazon

Research

I work in the fields of data mining & machine learning and enjoy both formulating problems and applying rigorous mathematical techniques to address them. I particularly like applying these techniques to problems of social importance (typically scientific data), my current focus is mining the results of pandemic micro-simulation data for pandemic preparation (with Virginia Tech’s VBI). I’ve divided my papers by problem area below. I have made use of techniques from information theory, probability, statistics and worst-case complexity analysis and am interested in investigating the application of new techniques to DM and AI problems.

I have several research assistantship slots available for students who are motivated to do research in data mining. My research is a mix of rigorous algorithm design, implementation and application to novel areas of social importance. Please read some of my work and contact me if your genuinely interested. Do NOT send form letters.

I bought the constrained-clustering.org domain name and its being set up as a central repository for things related to clustering with constraints. So far it only contains my work but it will be expanded. Feel free to email me for suggestions.

Personal Interests

If you would like to read about me and my personal interests then click here.

Awards

Best research paper award at SIAM Data Mining Conference 2005 

Best application paper award IEEE Data Mining Conference 2006.

I am most thankful for the NSF for supporting my work via a NSF CAREER Award 2007-2011. Project Webpage Google Research Award 2008

Ph.D. Students

I like working with undergraduates and graduate students, so email me if your interested in my research areas.

Current Students

Zijie Qie (2007-)
Xiang Wang (2008-)
Haifeng Zhao (2008-) 

Alumni

Ashwin Satyanarayana (SUNY) Graduated July 31st 2006. Congratulations to Ashwin for Winning a Distinguished Dissertation Award from the University. First employment : Microsoft Research - Datamining and Exploration Group.
Ke Yin. (SUNY) Graduated Jan 15^th 2007 . First employment : Glaxo Research – Data Mining and Drug Design

Recent Books

Constrained Clustering: Advances in Algorithms, Applications and Theory, In Preparation Due out 2008 co-edited with Sugato Basu and Kiri Wagstaff. CRC Press. Click here for book details, Buy from Amazon
Knowledge Discovery and Data Mining: Challenges and Realities of Mining Real World Data with co-edited with Hill Zhu, Ideal Press 2007. Click here for ToC

Tutorials

IEEE ICDM 2005 Clustering with Constraints – Theory and Practice (with S.Basu SRI)

ACM KDD 2006  Clustering with Constraints – Theory and Practice (with S.Basu SRI) PDF of Slides

 

Recent and Upcoming Research Activities

PC Co-Chair Workshop on Data Mining with Constraints - co-located with ACM KDD Conference, 2008. Workshop Webpage

Area Chair: ECML/PKDD 2008

Co-Workshop Chair, 9th SIAM Conference on Data Mining 2009.

Area Chair: ECML/PKDD 2008

Tutorial Chair, 8th SIAM Conference on Data Mining 2008.

Publicity Chair, 6th SIAM Conference on DM 2006.

Co-Guest Editor, Special Issue on Mining Low Quality Data, KAIS: Click here for CFP

PC Member: ACM SIGKDD Conference (KDD07, 08),      ACM CIKM Conference, 2006,2007               
Pacific Asia KDDM Conf. (PAKDD06,07)
AAAI Conference (AAAI07,06)                      IEEE  DM Conference, (ICDM07)

SIAM Conference on DM 2006,2007,            ECML/PKDD, (2005,2006,2007)

I also review for Pattern Recognition Letters, IEEE KDE, KAIS, IEEE PAMI and JMLR amongst others.

 

Select Recent Publications by Area

Many papers have a journal or technical report analogs which contain proofs and derivations that could not fit into the conference version as well as additional results. Please email me for pointers to these extended versions.

Constrained Clustering (In collaboration with S.  Basu (SRI), S.S. Ravi (SUNY) and K. Wagstaff (NASA-JPL))

Davidson I. Ester M. and Ravi, S.S., Efficient Incremental Clustering with Constraints, 13th ACM Knowledge Discovery and Data Mining Conference 2007 PDF

Ge, R., Ester, M., Jin W., and Davidson I., Constraint Driven Clustering, 13th ACM Knowledge Discovery and Data Mining Conference 2007

Davidson I., Ravi, S.S., Intractability and Clustering with Constraints, To Appear in the Proceeding of ICML 2007 PDF

Davidson I., Basu S.S., Clustering with Constraints Bibliography, To Appear, PDF

Davidson I., Wagstaff, K., Basu, Sugato., Measuring Constraint-Set Utility for Partitional Clustering Algorithms , To Appear in the Proceeding of ECML/PKDD 2006 (acceptance rate 9%) PDF,
Davidson I., Ravi S.S., Identifying and Generating Easy Sets of Constraints For Clustering, To Appear 21^st AAAI Conference, 2006. (acceptance rate 21%) PDF  

Davidson I. and Ravi, S. S. Hierarchical Clustering with Constraints: Theory and Practice, 9th European Principles and Practice of KDD, PKDD 2005. (acceptance rate 11%) (Email me for Journal/TR version) PDF Extended technical report with all proofs PDF

Davidson I., Ravi, S.S., Clustering under Constraints: Feasibility Issues and the $K$-Means Algorithm, 5th SIAM Data Mining Conference, (acceptance rate 14%). Winner, Best Paper Award, PDF,

Learning With Biased Training Sets (In collaboration with Wei Fan (IBM Watson))

Wei Fan and Ian Davidson "On Sample Selection Bias and Its Efficient Correction via Model Averaging and Unlabeled Examples", SIAM Data Mining Conference 2007 (acceptance rate 9%).

Kun Zhang, Wei Fan, Xiaojing Yuan, Ian Davidson, and Xiangshang Li, "Forecasting Skewed Biased Stochastic Ozone Days", IEEE ICDM 2006 (acceptance rate 9%). , Winner Best Paper Award, PDF,
Davidson I., Fan, Wei., When Efficient Model Averaging Out-Performs Boosting and Bagging , To Appear in the Proceeding of ECML/PKDD 2006 (acceptance rate 18%) PDF,
Fan, W. and Davidson I., ReverseTesting: An Efficient Framework to Select Amongst Classifiers under Sample Selection Bias, To Appear 12^th ACM KDD Conference, Philadelphia, 2006. (acceptance rate 11%) PDF  

Fan W., Davidson I., Zadrozny B. and Yu P., An Improved Categorization of Classifier's Sensitivity on Sample Selection Bias, 5th IEEE International Conference on Data Mining, ICDM 2005. (acceptance rate 18%) PDF  

Applications

Davidson I., Paul, Goutam, Locating Secret Messages in Images, Research Track: 10th ACM KDD Conference 2004: Research Track, Seattle (acceptance rate 29%) PDF
Davidson I. et al, A General Approach to Incorporate Data Quality Matrices into Data Mining Algorithms, 10th ACM KDD Conference 2004: Industrial Track, 2004. (Email me for Journal/TR version). Seattle. PDF

Yin, K. and Davidson I., Visually Comparing Clustering Algorithms, 8th PKDD Conference 2004 (acceptance rate 27%), Sydney PDF

Berg G., Davidson I., Paul G., Duan M., Searching For Hidden Messages: Automatic Detection of Steganography, AAAI -  IAAI 2003 Pdf

Davidson, I., "Visualizing Clustering Results", SIAM International Conference on Data Mining, 2002 Color PDF

Foundations of Mining and Learning Using Information Theory, Probability and Statistics

Davidson I., Ensemble Approaches for Stable Learners with Convergence Bounds, 19th AAAI Conference 2004, San Jose (acceptance rate 26%) PDF.

Davidson, I., Yin, K., Message Length Estimators, Model Averaging and Optimal Prediction, Dimacs Workshop on Complexity and Inference (Yu, Hansen and Vitanyi), 2003. pdf

Davidson, I., and Aminian, M., Using The Central Limit Theorem for Belief Network Learning, Ian Davidson, Minoo Aminian, The 8th International Symposium on A.I. and Math pdf

Yin, K., Davidson I., Bayesian Model Averaging Across Model Spaces via Compact Encoding, The 8th International Symposium on A.I. and Math pdf

Davidson, I., "Minimum Message Length Clustering Using Gibbs Sampling", 16th International Conference on Uncertainty in A.I., 2000 PDF

Courses and Teaching

I mainly teach in the sub-fields of data mining and machine learning which are a very active area of research used extensively in the high tech industry though often under different titles. Data mining, speech recognition, natural language processing, machine learning and expert systems are all sub-fields of A.I. In addition applications of A.I. to specific domains are also popular such as bio-informatics, data mining and machine vision.

SUNY Courses

 

Spring 2005 CSI 445/661 – Data Mining and CSI 535 Introduction to Artificial Intelligence

Fall 2005 CSI 635 – A.I. II – Machine Learning

Spring 2006 CSI 431/531 – Data Mining and CSI 535 Introduction to Artificial Intelligence

For courses at U.C. Davis please see myucdavis course web pages.

This page is always under construction  

davidson at cs dot ucdavis dot edu