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Proceedings of the Fourth SIAM International Conference on Data Mining
Michael W. Berry, Umeshwar Dayal, Chandrika Kamath and David Skillicorn, Editors Proceedings in Applied Mathematics 117 Conference held April 2004, Lake Buena Vista, Florida The Fourth SIAM International Conference on Data Mining continues the tradition of providing an open forum for the presentation and discussion of innovative algorithms as well as novel applications of data mining. This is reflected in the talks by the four keynote speakers who will discuss data usability issues in systems for data mining in science and engineering, issues raised by new technologies that generate biological data, ways to find complex structured patterns in linked data, and advances in Bayesian inference techniques. This proceedings includes 61 research papers; 23 were accepted as poster presentations, 26 were accepted as regular papers, and 12 were accepted as student papers from the conference. Contents Message from the Program Co-Chairs Preface Mining Relationships between Interacting Episodes Carl Mooney and John F. Roddick Making Time-Series Classification More Accurate Using Learned Constraints Chotirat Ann Ratanamahatana and Eamonn Keogh GRM: A New Model for Clustering Linear Sequences Hansheng Lei and Venu Govindaraju Nonlinear Manifold Learning for Data Stream Martin H. C. Law, Nan Zhang, and Anil K. Jain Text Mining from Site Invariant and Dependent Features for Information Extraction Knowledge Adaptation Tak-Lam Wong and Wai Lam Constructing Time Decompositions for Analyzing Time Stamped Documents Parvathi Chundi and Daniel J. Rosenkrantz Equivalence of Several Two-Stage Methods for Linear Discriminant Analysis Peg Howland and Haesun Park A Framework for Discovering Co-location Patterns in Data Sets with Extended Spatial Objects Hui Xiong, Shashi Shekhar, Yan Huang, Vipin Kumar, Xiaobin Ma, and Jin Soung Yoo A Top-Down Method for Mining Most Specific Frequent Patterns in Biological Sequences Martin Ester and Xiang Zhang Using Support Vector Machines for Classifying Large Sets of Multi-Represented Objects Hans-Peter Kriegel, Peer Kröger, Alexej Pryakhin, and Matthias Schubert Minimum Sum-Squared Residue Co-Clustering of Gene Expression Data Hyuk Cho, Inderjit S. Dhillon, Yuqiang Guan, and Suvrit Sra Training Support Vector Machine Using Adaptive Clustering Daniel Boley and Dongwei Cao IREP++, A Faster Rule Learning Algorithm Oliver Dain, Robert K. Cunningham, and Stephen Boyer GenIc: A Single Pass Generalized Incremental Algorithm for Clustering Chetan Gupta and Robert Grossman CONQUEST: A Distributed Tool for Constructing Summaries of High-Dimensional Discrete Attributed Datasets Jie Chi, Mehmet Koyutürk, and Ananth Grama Basic Association Rules Guichong Li and Howard J. Hamilton Hierarchical Clustering for Thematic Browsing and Summarization of Large Sets of Association Rules Alípio Jorge Quantitative Evaluation of Clustering Results Using Computational Negative Controls Ronald K. Pearson, Tom Zylkin, James S. Schwaber, and Gregory E. Gonye An Abstract Weighting Framework for Clustering Algorithms Richard Nock and Frank Nielsen RBA: An Integrated Framework for Regression Based on Association Rules Aysel Ozgur, Pang-Ning Tan, and Vipin Kumar Privacy-Preserving Multivariate Statistical Analysis: Linear Regression and Classification Wenliang Du, Yunghsiang S. Han, and Shigang Chen Clustering with Bregman Divergences Arindam Banerjee, Srujana Merugu, Inderjit Dhillon, and Joydeep Ghosh Density-Connected Subspace Clustering for High-Dimensional Data Karin Kailing, Hans-Peter Kriegel, and Peer Kröger Tessellation and Clustering by Mixture Models and Their Parallel Implementations Qiang Du and Xiaoqiang Wang Clustering Categorical Data Using the Correlated-Force Ensemble Kun-Ta Chuang and Ming-Syan Chen HICAP: Hierarchical Clustering with Pattern Preservation Hui Xiong, Michael Steinbach, Pang-Ning Tan, and Vipin Kumar Enhancing Communities of Interest Using Bayesian Stochastic Blockmodels Deepak Agrawal and Daryl Pregibon VEDAS: A Mobile and Distributed Data Stream Mining System for Real-Time Vehicle Monitoring Hillol Kargupta, Ruchita Bhargava, Kun Liu, Michael Powers, Patrick Blair, Samuel Bushra, James Dull, Kakali Sarkar, Martin Klein, Mitesh Vasa, and David Handy DOMISA: DOM-Based Information Space Adsorption for Web Information Hierarchy Mining Hung-Yu Kao, Jan-Ming Ho, and Ming-Syan Chen CREDOS: Classification Using Ripple Down Structure (A Case for Rare Classes) Mahesh V. Joshi and Vipin Kumar Active Semi-Supervision for Pairwise Constrained Clustering Sugato Basu, Arindam Banerjee, and Raymond J. Mooney Finding Frequent Patterns in a Large Sparse Graph Michihiro Kuramochi and George Karypis A General Probabilistic Framework for Mining Labeled Ordered Trees Nobuhisa Ueda, Kiyoko F. Aoki, and Hiroshi Mamitsuka Mixture Density Mercer Kernels: A Method to Learn Kernels Directly from Data Ashok N. Srivastava A Mixture Model for Clustering Ensembles Alexander Topchy, Anil K. Jain, and William Punch Visualizing RFM Segmentation Ron Kohavi and Rajesh Parekh Visually Mining through Cluster Hierarchies Stefan Brechiesen, Hans-Peter Kriegel, Peer Kröger, and Martin Pfeifle Class-Specific Ensembles for Active Learning in Digital Imagery Amit Mandvikar and Huan Liu Mining Text for Word Senses Using Independent Component Analysis Reinhard Rapp A Kernel-Based Semi-Naive Bayesian Classifier Using P-Trees Anne Denton and William Perrizo BAMBOO: Accelerating Closed Itemset Mining by Deeply Pushing the Length-Decreasing Support Constraint Jianyong Wang and George Karypis A General Framework for Adaptive Anomaly Detection with Evolving Connectionist Systems Yihua Liao, V. Rao Vemuri, and Alejandro Pasos R-MAT: A Recursive Model for Graph Mining Deepayan Chakrabarti, Yiping Zhan, and Christos Faloutsos Lazy Learning by Scanning Memory Image Lattice Yiqiu Han and Wai Lam Text Mining Using Non-negative Matrix Factorizations V. Paul Pauca, Farial Shahnaz, Michael W. Berry, and Robert J. Plemmons Active Mining of Data Streams Wei Fan, Yi-an Huang, Haixun Wang, and Philip S. Yu Learning to Read Between the Lines: The Aspect Bernoulli Model A. Kabán, E. Bingham, and T. Hirsimäki Exploiting Hierarchical Domain Values in Classification Learning Yiqiu Han and Wai Lam IFD: Iterative Feature and Data Clustering Tao Li and Sheng Ma Adaptive Filtering for Efficient Record Linkage Lifang Gu and Rohan Baxter A Foundational Approach to Mining Itemset Utilities from Databases Hong Yao, Howard J. Hamilton, and Cory J. Butz The Discovery of Generalized Causal Models with Mixed Variables Using MML Criterion Gang Li and Honghua Dai Reservoir-Based Random Sampling with Replacement from Data Stream Byung-Hoon Park, George Ostrouchov, Nagiza F. Samatova, and Al Geist Principal Component Analysis and Effective K-Means Clustering Chris Ding and Xiaofeng He Classifying Documents without Labels Daniel Barbará, Carlotta Domeniconi, and Ning Kang Data Reduction in Support Vector Machines by a Kernelized Ionic Interaction Model Hyunsoo Kim and Haesun Park Continuous-Time Bayesian Modeling of Clinical Data Sathyakama Sandilya and R. Bharat Rao Subspace Clustering of High Dimensional Data Carlotta Domeniconi, Dimitris Papadopoulos, Dimitrios Gunopulos, and Sheng Ma Privacy Preserving Naïve Bayes Classifier for Vertically Partitioned Data Jaideep Vaidya and Chris Clifton Resource-Aware Mining with Variable Granularities in Data Streams Wei-Guang Teng, Ming-Syan Chen, and Philip S. Yu Mining Patters of Activity from Video Data Michael C. Burl Author Index 2004 / xiv + 537 / Softcover / ISBN-13: 978-0-898715-68-2 / ISBN-10: 0-89871-568-7 / List Price $144.50 / SIAM Member Price $101.15 / Order Code PR117
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Michael W. Berry, Umeshwar Dayal, Chandrika Kamath and David Skillicorn, Editors