4. SDM 2004: Orlando, Florida, USA

• Carl Mooney, John F. Roddick:
  Mining Relationships Between Interacting Episodes.
  
• Chotirat (Ann) Ratanamahatana, Eamonn J. Keogh:
  Making Time-Series Classification More Accurate Using Learned Constraints.
  
• Hansheng Lei:
  GRM: A New Model for Clustering Linear Sequences.
  
• Martin H. C. Law, Nan Zhang, Anil K. Jain:
  Nonlinear Manifold Learning for Data Stream.
  
• Wai Lam, Tak-Lam Wong:
  Text Mining from Site Invariant and Dependent Features for Information Extraction Knowledge Adaptation.
  
• Parvathi Chundi, Daniel J. Rosenkrantz:
  Constructing Time Decompositions for Analyzing Time-Stamped Documents.
  
• Peg Howland, Haesun Park:
  Equivalence of Several Two-Stage Methods for Linear Discriminant Analysis.
  
• Hui Xiong, Shashi Shekhar, Yan Huang, Vipin Kumar, Xiaobin Ma, Jin Soung Yoo:
  A Framework for Discovering Co-Location Patterns in Data Sets with Extended Spatial Objects.
  
• Martin Ester, Xiang Zhang:
  A Top-Down Method for Mining Most-Specific Frequent Patterns in Biological Sequences.
  
• Hans-Peter Kriegel, Peer Kröger, Alexey Pryakhin, Matthias Schubert:
  Using Support Vector Machines for Classifying Large Sets of Multi-Represented Objects.
  
• Hyuk Cho, Inderjit S. Dhillon, Yuqiang Guan, Suvrit Sra:
  Minimum Sum-Squared Residue Co-Clustering of Gene Expression Data.
  
• Daniel Boley, Dongwei Cao:
  Training Support Vector Machines Using Adaptive Clustering.
  
• Oliver Dain, Robert Cunningham, Stephen Boyer:
  IREP++, A Faster Rule Learning Algorithm.
  
• Chetan Gupta, Robert L. Grossman:
  GenIc: A Single-Pass Generalized Incremental Algorithm for Clustering.
  
• Jie Chi, Mehmet Koyutürk, Ananth Grama:
  Conquest: A Distributed Tool for Constructing Summaries of High-Dimensional Discrete Attribute Data Sets.
  
• Guichong Li, Howard J. Hamilton:
  Basic Association Rules.
  
• Alípio Jorge:
  Hierarchical Clustering for Thematic Browsing and Summarization of Large Sets of Association Rules.
  
• Ronald K. Pearson, Tom Zylkin, James S. Schwaber, Gregory E. Gonye:
  Analytical Evaluation of Clustering Results Using Computational Negative Controls.
  
• Richard Nock, Frank Nielsen:
  An Abstract Weighting Framework for Clustering Algorithms.
  
• Aysel Ozgur, Pang-Ning Tan, Vipin Kumar:
  RBA: An Integrated Framework for Regression based on Association Rules.
  
• Wenliang Du, Yunghsiang S. Han, Shigang Chen:
  Privacy-Preserving Multivariate Statistical Analysis: Linear Regression and Classification.
  
• Arindam Banerjee, Srujana Merugu, Inderjit S. Dhillon, Joydeep Ghosh:
  Clustering with Bregman Divergences.
  
• Peer Kröger, Hans-Peter Kriegel, Karin Kailing:
  Density-Connected Subspace Clustering for High-Dimensional Data.
  
• Qiang Du, Xiaoqiang Wang:
  Tesselation and Clustering by Mixture Models and Their Parallel Implementations.
  
• Ming-Syan Chen, Kun-Ta Chuang:
  Clustering Categorical Data Using the Correlated-Force Ensemble.
  
• Hui Xiong, Michael Steinbach, Pang-Ning Tan, Vipin Kumar:
  HICAP: Hierarchical Clustering with Pattern Preservation.
  
• Deepak Agrawal, Daryl Pregibon:
  Enhancing Communities of Interest Using Bayesian Stochastic Blockmodels.
  
• Hillol Kargupta, Ruchita Bhargava, Kun Liu, Michael Powers, Patrick Blair, Samuel Bushra, James Dull, Kakali Sarkar, Martin Klein, Mitesh Vasa, David Handy:
  VEDAS: A Mobile and Distributed Data Stream Mining System for Real-Time Vehicle Monitoring.
  
• Hung-Yu Kao, Jan-Ming Ho, Ming-Syan Chen:
  DOMISA: DOM-Based Information Space Adsorption of Web Information Hierarchy Mining.
  
• Mahesh V. Joshi, Vipin Kumar:
  CREDOS: Classification Using Ripple Down Structure (A Case for Rare Classes).
  
• Sugato Basu, Arindam Banerjee, Raymond J. Mooney:
  Active Semi-Supervision for Pairwise Constrained Clustering.
  
• Michihiro Kuramochi, George Karypis:
  Finding Frequent Patterns in a Large Sparse Graph.
  
• Nobuhisa Ueda, Kiyoko F. Aoki, Hiroshi Mamitsuka:
  A General Probabilistic Framework for Mining Labeled Ordered Trees.
  
• Ashok N. Srivastava:
  Mixture Density Mercer Kernels: A Method to Learn Kernels Directly from Data.
  
• Alexander P. Topchy, Anil K. Jain, William F. Punch:
  A Mixture Model for Clustering Ensembles.
  
• Ron Kohavi, Rajesh Parekh:
  Visualizing RFM Segmentation.
  
• Stefan Brecheisen, Hans-Peter Kriegel, Peer Kröger, Martin Pfeifle:
  Visually Mining through Cluster Hierarchies.
  
• Amit Mandvikar, Huan Liu:
  Class-Specific Ensembles for Active Learning.
  
• Reinhard Rapp:
  Mining Text for Word Senses Using Independent Component Analysis.
  
• Anne Denton, William Perrizo:
  A Kernel-Based Semi-Naïve Bayesian Classifier Using P-Trees.
  
• Jianyong Wang, George Karypis:
  BAMBOO: Accelerating Closed Itemset Mining by Deeply Pushing the Length-Decreasing Support Constraint.
  
• Yihua Liao, V. Rao Vemuri, Alejandro Pasos:
  A General Framework for Adaptive Anomaly Detection with Evolving Connectionist Systems.
  
• Deepayan Chakrabarti, Yiping Zhan, Christos Faloutsos:
  R-MAT: A Recursive Model for Graph Mining.
  
• Yiqiu Han, Wai Lam:
  Lazy Learning by Scanning Memory Image Lattice.
  
• V. Paul Pauca, Farial Shahnaz, Michael W. Berry, Robert J. Plemmons:
  Text Mining Using Non-Negative Matrix Factorizations.
  
• Wei Fan, Yi-an Huang, Haixun Wang, Philip S. Yu:
  Active Mining of Data Streams.
  
• Ata Kabán, Ella Bingham, T. Hirsimäki:
  Learning to Read Between the Lines: The Aspect Bernoulli Model.
  
• Yiqiu Han, Wai Lam:
  Exploiting Hierarchical Domain Values in Classification Learning.
  
• Tao Li, Sheng Ma:
  IFD: Iterative Feature and Data Clustering.
  
• Lifang Gu, Rohan A. Baxter:
  Adaptive Filtering for Efficient Record Linkage.
  
• Hong Yao, Howard J. Hamilton, Cory J. Butz:
  A Foundational Approach to Mining Itemset Utilities from Databases.
  
• Gang Li, Honghua Dai:
  The Discovery of Generalized Causal Models with Mixed Variables Using MML Criterion.
  
• Byung-Hoon Park, George Ostrouchov, Nagiza F. Samatova, Al Geist:
  Reservoir-Based Random Sampling with Replacement from Data Stream.
  
• Chris H. Q. Ding, Xiaofeng He:
  Principal Component Analysis and Effective K-Means Clustering.
  
• Daniel Barbará, Carlotta Domeniconi, Ning Kang:
  Classifying Documents Without Labels.
  
• Hyunsoo Kim, Haesun Park:
  Data Reduction in Support Vector Machines by a Kernelized Ionic Interaction Model.
  
• Sathyakama Sandilya, R. Bharat Rao:
  Continuous-Time Bayesian Modeling of Clinical Data.
  
• Carlotta Domeniconi, Dimitris Papadopoulos, Dimitrios Gunopulos, Sheng Ma:
  Subspace Clustering of High Dimensional Data.
  
• Jaideep Vaidya, Chris Clifton:
  Privacy Preserving Naïve Bayes Classifier for Vertically Partitioned Data.
  
• Wei-Guang Teng, Ming-Syan Chen, Philip S. Yu:
  Resource-Aware Mining with Variable Granularities in Data Streams.
  
• Michael C. Burl:
  Mining Patters of Activity from Video Data.
  

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