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KDD-2000 Sixth ACM SIGKDD International Conference on KDD-2000 Accepted Research PapersNote: if all authors have the same affiliation, the affiliation is listed following the last author. Papers are ordered by their ID number. Depth First Generation of Long Patterns (#102). C. Aggarwal, R. Agarwal, and V. V. V. Prasad (IBM T. J. Watson Research Center).
Reversing the Dimensionality Curse for Similarity Indexing in High Dimensional Space (#105). C. Aggarwal and P. Yu (IBM T. J. Watson Research Center).
Mining IC Test Data to Optimize VLSI Testing (#125). Best Application Paper T. Fountain (San Diego Supercomputer Center, UCSD), T. Dietterich (Oregon State University), and B. Sudyka (Hewlett Packard Company).
Deformable Markov Model Templates for Time-Series Pattern Matching (#154). Runner-Up Best Research Paper X. Ge and P. Smyth (University of California, Irvine).
A General Probabilistic Framework for Clustering Individuals (#160). I. Cadez, S. Gaffney, P. Smyth (University of California, Irvine).
A Framework for Specifying Explicit Bias for Revision of Approximate Information Extraction Rules (#185) R. Feldman, J. Scler, and Y. Liberzon (Instinct Software).
Efficient Search for Association Rules (#211). G. Webb (Deakin University).
Generating Non-Redundant Association Rules (#235). M. Zaki (Rensselaer Polytechnic Institute).
Multi-Level Organization and Summarization of the Discovered Rules (#247). B. Liu, M. Hu, and W. Hsu (National University of Singapore).
Towards an Effective Cooperation on the Computer and the User for Classification (#248). M. Ankerst, M. Ester, and H.-P. Kriegel (University of Munich).
Visualizing Association Rules with Interactive Mosaic Plots (#255). H. Hofmann (Augsburg University), A. Siebes (CWI), and A. Wilhelm (Augsburg University).
Efficient Clustering of High-Dimensional Datasets with Application to Reference Matching (#256). A. McCallum (Just Research), K. Nigam (Carnegie Mellon University), and L. Ungar (University of Pennsylvania).
Active Learning Using Adaptive Resampling (#262). V. Iyengar, C. Apte, and T. Zhang (IBM Research).
Global Partial Orders from Sequential Data (#273). H. Mannila (Nokia) and C. Meek (Microsoft Research).
RuleViz: A Model for Visualizing Knowledge Discovery Process (#276). J. Han and N. Cercone (University of Waterloo).
Efficient Identification of Web Communities (#298). G. Flake, S. Lawrence, and C. L. Giles (NEC Research Institute).
Ongoing Management and Application of Discovered Knowledge in a Large Regulatory Organization: A Case Study of the Use and Impact of NASD Regulation's Advanced Detection System (ADS) (#310). Runner-Up Best Application Paper T. Senator (NASD Regulation, Inc.).
Visualization and the Process of Modeling: A Cognitive-Theoretic View (#314). A. W. Crapo (GE Corporate Research & Development, Rensselaer Polytechnic Institute), L. B. Waisel (Carnegie Mellon University), W. A. Wallace (Rensselaer Polytechnic Institute), T. R. Willemain (Rensselaer Polytechnic Institute).
Hancock: A Language for Extracting Signatures from Data Streams (#321). Best Research Paper C. Cortes (AT&T Labs-Research), K. Fisher (AT&T Labs-Research), D. Pregibon (AT&T Labs-Research), A. Rogers (AT&T Labs-Research), and F. Smith (Cornell University).
Data Selection for Support Vector Machine Classifiers (#324). O. Mangasarian and G. Fung (University of Wisconsin).
Small is Beautiful: Discovering the Minimal Set of Unexpected Patterns (#328). B. Padmanabhan (University of Pennsylvania) and A. Tuzhilin (New York University).
Explicity Representing Expected Cost: An Alternative to ROC Representation (#331). C. Drummond and R. Holte (University of Ottawa).
Mining High-Speed Data Streams (#339). P. Domingos and G. Hulten (University of Washington).
An Empirical Analysis of Techniques for Constructing and Searching K-Dimensional Trees (#350). D. Talbert and D. Fisher (Vanderbilt University).
The Generalized Bayesian Committee Machine (#384). V. Tresp (Siemens).
Interactive Exploration of Very Large Relational Data Sets Through 3D Dynamic Projections (#392). L. Yang (Western Michigan University). Hardening Soft Information Sources (#115). W. Cohen, H. Kautz, and D. McAllester (AT&T Labs).
Using the Fractal Dimension to Cluster Datasets (#116). D. Barbara and P. Chen (George Mason University).
Growing Decision Trees on Association Rules (#131). K. Wang, S. Zhou, and Y. He (National University of Singapore).
Efficient Mining of Weighted Association Rules (WAR) (#162). J. Yang (IBM), W. Wang (IBM), and P. Yu (IBM T. J. Watson Research Center).
Mining Asynchronous Period Pattern in Time Series Data (#163). J. Yang (IBM), W. Wang (IBM), and P. Yu (IBM T. J. Watson Research Center).
Visualization of Navigation Patterns on a Web Site Using Model Based Clustering (#164)
Scaling Up Dynamic Time Warping for Data Mining Applications (#166) E. Keogh and M. Pazzani (University of California, Irvine).
IntelliClean: A Knowledge-Based Intelligent Data Cleaner (#168) W. L. Low, M. L. Lee, and T. W. Ling (National University of Singapore).
Towards Scalable Support Vector Machines using Squashing (#171) D. Pavlov, D. Chudova, and P. Smyth (University of California, Irvine).
A Data Mining Framework for Optimal Product Selection in Retail Supermarket Data: The Generalized PROFSET Model (#188) T. Brijs, G. Swinnen, K. Vanhoof, G. Wets, and B. Goethals (Limburg University Centre).
Application of Neural Networks to Biological Data Mining: A Case Study in Protein Sequence Classification (#192). J. T. L. Wang (New Jersey Institute of Technology), Q. Ma (New Jersey Institute of Technology), D. Shasha (New York University), and C. H. Wu (Georgetown University Medical Center).
Exploring Constraints to Efficiently Mine Emerging Patters from Large High-Dimensional Datasets (#196). X. Zhang (University of Melbourne), G. Dong (Wright State University), and K. Ramamohanarao (University of Melbourne).
Multivariate Discretization of Continuous Variables for Set Mining (#198). S. Bay (University of California, Irvine).
On-Line Unsupervised Outlier Detection Using Finite Mixtures with Discounting Learning Algorithms (#214). K. Yamanishi (NEC Corporation), J. Takeuchi (NEC Corporation), G. Williams (ACSys Data Mining Program), and P. Milne (CSIRO).
Unsupervised Bayesian Visualization of High-Dimensional Data (#253). H. Tirri, P. Kontkanen, J. Lahtinen, and P. Myllym�ki (University of Helsinki).
A Sequential Sampling Algorithm for a General Class of Utility Criteria (#270). T. Scheffer and S. Wrobel (University of Magdeburg).
Efficient Algorithms for Constructing Decision Trees with Constraints (#296). K. Shim (KAIST), M. Garofalakis (Bell Laboratories), D. Hyun (KAIST), and R. Rastogi (Lucent Technologies).
A Classifier for Semi-Structured Documents (#300). J. Yi and N. Sundaresan (IBM Almaden).
Apha Seeding for Support Vector Machines (#313). D. DeCoste (JPL/Caltech) and K. Wagstaff (Cornell University).
Can We Push More Constraints Into Frequent Pattern Mining? (#335). J. Pei and J. Han (Simon Fraser University).
FreeSpan: Frequent Pattern-Projected Sequential Pattern Mining (#338). J. Pei (Simon Fraser University), J. Han (Simon Fraser University), B. Mortazavi-Asl (Simon Fraser University), Q. Chen (Hewlett-Packard), U. Dayal (Hewlett-Packard), and M.-C. Hsu (Hewlett Packard).
Visualization and Interactive Feature Selection for Unsupervised Data (#362). J. G. Dy and C. E. Brodley (Purdue University).
Feature Selection in Unsupervised Learning via Evolutionary Search (#364). N. Street, Y.-S. Kim, and F. Menczer (University of Iowa).
Visualization and Data Mining of High Dimensional Data (#389). A. Inselberg (Tel Aviv University). |
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