Table of Contents

• Mining Relationships Between Interacting EpisodesCarl Mooney, John F. Roddick. 1-10 [doi]
• Making Time-Series Classification More Accurate Using Learned ConstraintsChotirat (Ann) Ratanamahatana, Eamonn J. Keogh. 11-22 [doi]
• GRM: A New Model for Clustering Linear SequencesHansheng Lei. 23-32 [doi]
• Nonlinear Manifold Learning for Data StreamMartin H. C. Law, Nan Zhang 0002, Anil K. Jain. 33-44 [doi]
• Text Mining from Site Invariant and Dependent Features for Information Extraction Knowledge AdaptationWai Lam, Tak-Lam Wong. 45-56 [doi]
• Constructing Time Decompositions for Analyzing Time-Stamped DocumentsParvathi Chundi, Daniel J. Rosenkrantz. 57-68 [doi]
• Equivalence of Several Two-Stage Methods for Linear Discriminant AnalysisPeg Howland, Haesun Park. 69-77 [doi]
• A Framework for Discovering Co-Location Patterns in Data Sets with Extended Spatial ObjectsHui Xiong, Shashi Shekhar, Yan Huang, Vipin Kumar, Xiaobin Ma, Jin Soung Yoo. 78-89 [doi]
• A Top-Down Method for Mining Most-Specific Frequent Patterns in Biological SequencesMartin Ester, Xiang Zhang. 90-101 [doi]
• Using Support Vector Machines for Classifying Large Sets of Multi-Represented ObjectsHans-Peter Kriegel, Peer Kröger, Alexey Pryakhin, Matthias Schubert. 102-113 [doi]
• Minimum Sum-Squared Residue Co-Clustering of Gene Expression DataHyuk Cho, Inderjit S. Dhillon, Yuqiang Guan, Suvrit Sra. 114-125 [doi]
• Training Support Vector Machines Using Adaptive ClusteringDaniel Boley, Dongwei Cao. 126-137 [doi]
• IREP++, A Faster Rule Learning AlgorithmOliver Dain, Robert Cunningham, Stephen Boyer. 138-146 [doi]
• GenIc: A Single-Pass Generalized Incremental Algorithm for ClusteringChetan Gupta, Robert L. Grossman. 147-153 [doi]
• Conquest: A Distributed Tool for Constructing Summaries of High-Dimensional Discrete Attribute Data SetsJie Chi, Mehmet Koyutürk, Ananth Grama. 154-165 [doi]
• Basic Association RulesGuichong Li, Howard J. Hamilton. 166-177 [doi]
• Hierarchical Clustering for Thematic Browsing and Summarization of Large Sets of Association RulesAlípio Jorge. 178-187 [doi]
• Analytical Evaluation of Clustering Results Using Computational Negative ControlsRonald K. Pearson, Tom Zylkin, James S. Schwaber, Gregory E. Gonye. 188-199 [doi]
• An Abstract Weighting Framework for Clustering AlgorithmsRichard Nock, Frank Nielsen. 200-209 [doi]
• RBA: An Integrated Framework for Regression based on Association RulesAysel Ozgur, Pang-Ning Tan, Vipin Kumar. 210-221 [doi]
• Privacy-Preserving Multivariate Statistical Analysis: Linear Regression and ClassificationWenliang Du, Yunghsiang S. Han, Shigang Chen. 222-233 [doi]
• Clustering with Bregman DivergencesArindam Banerjee, Srujana Merugu, Inderjit S. Dhillon, Joydeep Ghosh. 234-245 [doi]
• Density-Connected Subspace Clustering for High-Dimensional DataPeer Kröger, Hans-Peter Kriegel, Karin Kailing. 246-256 [doi]
• Tesselation and Clustering by Mixture Models and Their Parallel ImplementationsQiang Du, Xiaoqiang Wang. 257-268 [doi]
• Clustering Categorical Data Using the Correlated-Force EnsembleMing-Syan Chen, Kun-Ta Chuang. 269-278 [doi]
• HICAP: Hierarchical Clustering with Pattern PreservationHui Xiong, Michael Steinbach, Pang-Ning Tan, Vipin Kumar. 279-290 [doi]
• Enhancing Communities of Interest Using Bayesian Stochastic BlockmodelsDeepak Agrawal, Daryl Pregibon. 291-299 [doi]
• VEDAS: A Mobile and Distributed Data Stream Mining System for Real-Time Vehicle MonitoringHillol Kargupta, Ruchita Bhargava, Kun Liu, Michael Powers, Patrick Blair, Samuel Bushra, James Dull, Kakali Sarkar, Martin Klein, Mitesh Vasa, David Handy. 300-311 [doi]
• DOMISA: DOM-Based Information Space Adsorption of Web Information Hierarchy MiningHung-Yu Kao, Jan-Ming Ho, Ming-Syan Chen. 312-320 [doi]
• CREDOS: Classification Using Ripple Down Structure (A Case for Rare Classes)Mahesh V. Joshi, Vipin Kumar. 321-332 [doi]
• Active Semi-Supervision for Pairwise Constrained ClusteringSugato Basu, Arindam Banerjee, Raymond J. Mooney. 333-344 [doi]
• Finding Frequent Patterns in a Large Sparse GraphMichihiro Kuramochi, George Karypis. 345-356 [doi]
• A General Probabilistic Framework for Mining Labeled Ordered TreesNobuhisa Ueda, Kiyoko F. Aoki, Hiroshi Mamitsuka. 357-368 [doi]
• Mixture Density Mercer Kernels: A Method to Learn Kernels Directly from DataAshok N. Srivastava. 369-378 [doi]
• A Mixture Model for Clustering EnsemblesAlexander P. Topchy, Anil K. Jain, William F. Punch. 379-390 [doi]
• Visualizing RFM SegmentationRon Kohavi, Rajesh Parekh. 391-399 [doi]
• Visually Mining through Cluster HierarchiesStefan Brecheisen, Hans-Peter Kriegel, Peer Kröger, Martin Pfeifle. 400-411 [doi]
• Class-Specific Ensembles for Active LearningAmit Mandvikar, Huan Liu. 412-421 [doi]
• Mining Text for Word Senses Using Independent Component AnalysisReinhard Rapp. 422-426 [doi]
• A Kernel-Based Semi-Naïve Bayesian Classifier Using P-TreesAnne Denton, William Perrizo. 427-431 [doi]
• BAMBOO: Accelerating Closed Itemset Mining by Deeply Pushing the Length-Decreasing Support ConstraintJianyong Wang, George Karypis. 432-436 [doi]
• A General Framework for Adaptive Anomaly Detection with Evolving Connectionist SystemsYihua Liao, V. Rao Vemuri, Alejandro Pasos. 437-441 [doi]
• R-MAT: A Recursive Model for Graph MiningDeepayan Chakrabarti, Yiping Zhan, Christos Faloutsos. 442-446 [doi]
• Lazy Learning by Scanning Memory Image LatticeYiqiu Han, Wai Lam. 447-451 [doi]
• Text Mining Using Non-Negative Matrix FactorizationsV. Paul Pauca, Farial Shahnaz, Michael W. Berry, Robert J. Plemmons. 452-456 [doi]
• Active Mining of Data StreamsWei Fan, Yi-an Huang, Haixun Wang, Philip S. Yu. 457-461 [doi]
• Learning to Read Between the Lines: The Aspect Bernoulli ModelAta Kabán, Ella Bingham, T. Hirsimäki. 462-466 [doi]
• Exploiting Hierarchical Domain Values in Classification LearningYiqiu Han, Wai Lam. 467-471 [doi]
• IFD: Iterative Feature and Data ClusteringTao Li, Sheng Ma. 472-476 [doi]
• Adaptive Filtering for Efficient Record LinkageLifang Gu, Rohan A. Baxter. 477-481 [doi]
• A Foundational Approach to Mining Itemset Utilities from DatabasesHong Yao, Howard J. Hamilton, Cory J. Butz. 482-486 [doi]
• The Discovery of Generalized Causal Models with Mixed Variables Using MML CriterionGang Li, Honghua Dai. 487-491 [doi]
• Reservoir-Based Random Sampling with Replacement from Data StreamByung-Hoon Park, George Ostrouchov, Nagiza F. Samatova, Al Geist. 492-496 [doi]
• Principal Component Analysis and Effective K-Means ClusteringChris H. Q. Ding, Xiaofeng He. 497-501 [doi]
• Classifying Documents Without LabelsDaniel Barbará, Carlotta Domeniconi, Ning Kang. 502-506 [doi]
• Data Reduction in Support Vector Machines by a Kernelized Ionic Interaction ModelHyunsoo Kim, Haesun Park. 507-511 [doi]
• Continuous-Time Bayesian Modeling of Clinical DataSathyakama Sandilya, R. Bharat Rao. 512-516 [doi]
• Subspace Clustering of High Dimensional DataCarlotta Domeniconi, Dimitris Papadopoulos, Dimitrios Gunopulos, Sheng Ma. 517-521 [doi]
• Privacy Preserving Naïve Bayes Classifier for Vertically Partitioned DataJaideep Vaidya, Chris Clifton. 522-526 [doi]
• Resource-Aware Mining with Variable Granularities in Data StreamsWei-Guang Teng, Ming-Syan Chen, Philip S. Yu. 527-531 [doi]
• Mining Patters of Activity from Video DataMichael C. Burl. 532-536 [doi]