5. ICDM 2005: Houston, Texas, USA

Introduction

• Welcome Message from the Conference Chairs.
  
• Welcome to ICDM 2005.
  
• Conference Organization.
  
• Steering Committee.
  
• Program Committee.
  
• Non-PC Reviewers.
  
• Invited Talks.
  
• Tutorials.
  
• Workshops.
  
• Panel Session.
  

Regular Papers

• Alan S. Abrahams, Adrian Becker, Daniel Fleder, Ian C. MacMillan:
  Handling Generalized Cost Functions in the Partitioning Optimization Problem through Sequential Binary Programming. 3-9
  
• Elke Achtert, Christian Böhm, Hans-Peter Kriegel, Peer Kröger:
  Online Hierarchical Clustering in a Data Warehouse Environment. 10-17
  
• Manu Aery, Sharma Chakravarthy:
  eMailSift: Email Classification Based on Structure and Content. 18-25
  
• Deepak K. Agarwal:
  An Empirical Bayes Approach to Detect Anomalies in Dynamic Multidimensional Arrays. 26-33
  
• Costin Barbu, Raja Tanveer Iqbal, Jing Peng:
  Classifier Fusion Using Shared Sampling Distribution for Boosting. 34-41
  
• Steven M. Beitzel, Eric C. Jensen, Ophir Frieder, David D. Lewis, Abdur Chowdhury, Aleksander Kolcz:
  Improving Automatic Query Classification via Semi-Supervised Learning. 42-49
  
• Arnab Bhattacharya, Vebjorn Ljosa, Jia-Yu Pan, Mark R. Verardo, Hyung-Jeong Yang, Christos Faloutsos, Ambuj K. Singh:
  ViVo: Visual Vocabulary Construction for Mining Biomedical Images. 50-57
  
• Mikhail Bilenko, Sugato Basu, Mehran Sahami:
  Adaptive Product Normalization: Using Online Learning for Record Linkage in Comparison Shopping. 58-65
  
• Julien Blanchard, Fabrice Guillet, Régis Gras, Henri Briand:
  Using Information-Theoretic Measures to Assess Association Rule Interestingness. 66-73
  
• Karsten M. Borgwardt, Hans-Peter Kriegel:
  Shortest-Path Kernels on Graphs. 74-81
  
• Huiping Cao, Nikos Mamoulis, David W. Cheung:
  Mining Frequent Spatio-Temporal Sequential Patterns. 82-89
  
• Philip K. Chan, Matthew V. Mahoney:
  Modeling Multiple Time Series for Anomaly Detection. 90-97
  
• Varun Chandola, Vipin Kumar:
  Summarization - Compressing Data into an Informative Representation. 98-105
  
• Hung-Leng Chen, Kun-Ta Chuang, Ming-Syan Chen:
  Labeling Unclustered Categorical Data into Clusters Based on the Important Attribute Values. 106-113
  
• Jason R. Chen:
  Making Subsequence Time Series Clustering Meaningful. 114-121
  
• Anne Denton:
  Kernel-Density-Based Clustering of Time Series Subsequences Using a Continuous Random-Walk Noise Model. 122-129
  
• Magdalini Eirinaki, Michalis Vazirgiannis:
  Usage-Based PageRank for Web Personalization. 130-137
  
• Mohamed G. Elfeky, Walid G. Aref, Ahmed K. Elmagarmid:
  WARP: Time Warping for Periodicity Detection. 138-145
  
• Mohammad El-Hajj, Osmar R. Zaïane, Paul Nalos:
  Bifold Constraint-Based Mining by Simultaneous Monotone and Anti-Monotone Checking. 146-153
  
• Wei Fan, Ed Greengrass, Joe McCloskey, Philip S. Yu, Kevin Drummey:
  Effective Estimation of Posterior Probabilities: Explaining the Accuracy of Randomized Decision Tree Approaches. 154-161
  
• Frédéric Flouvat, Fabien De Marchi, Jean-Marc Petit:
  A Thorough Experimental Study of Datasets for Frequent Itemsets. 162-169
  
• Shohei Hido, Hiroyuki Kawano:
  AMIOT: Induced Ordered Tree Mining in Tree-Structured Databases. 170-177
  
• Yi Huang, Kai Yu, Matthias Schubert, Shipeng Yu, Volker Tresp, Hans-Peter Kriegel:
  Hierarchy-Regularized Latent Semantic Indexing. 178-185
  
• Koji Iwanuma, Ryuichi Ishihara, Yo Takano, Hidetomo Nabeshima:
  Extracting Frequent Subsequences from a Single Long Data Sequence: A Novel Anti-Monotonic Measure and a Simple On-Line Algorithm. 186-193
  
• Xiaonan Ji, James Bailey, Guozhu Dong:
  Mining Minimal Distinguishing Subsequence Patterns with Gap Constraints. 194-201
  
• Liangxiao Jiang, Harry Zhang:
  Learning Instance Greedily Cloning Naive Bayes for Ranking. 202-209
  
• Ruoming Jin, Gagan Agrawal:
  An Algorithm for In-Core Frequent Itemset Mining on Streaming Data. 210-217
  
• Alexandros Kalousis, Julien Prados, Melanie Hilario:
  Stability of Feature Selection Algorithms. 218-225
  
• Eamonn J. Keogh, Jessica Lin, Ada Wai-Chee Fu:
  HOT SAX: Efficiently Finding the Most Unusual Time Series Subsequence. 226-233
  
• Effrosini Kokiopoulou, Yousef Saad:
  Orthogonal Neighborhood Preserving Projections. 234-241
  
• Tamara G. Kolda, Brett W. Bader, Joseph P. Kenny:
  Higher-Order Web Link Analysis Using Multilinear Algebra. 242-249
  
• Hans-Peter Kriegel, Peer Kröger, Matthias Renz, Sebastian Wurst:
  A Generic Framework for Efficient Subspace Clustering of High-Dimensional Data. 250-257
  
• Hans-Peter Kriegel, Peer Kröger, Alexey Pryakhin, Matthias Schubert:
  Effective and Efficient Distributed Model-Based Clustering. 258-265
  
• Daesu Lee, Wonsuk Lee:
  Finding Maximal Frequent Itemsets over Online Data Streams Adaptively. 266-273
  
• Carson Kai-Sang Leung, Quamrul I. Khan, Tariqul Hoque:
  CanTree: A Tree Structure for Efficient Incremental Mining of Frequent Patterns. 274-281
  
• Bo Long, Zhongfei (Mark) Zhang, Philip S. Yu:
  Combining Multiple Clusterings by Soft Correspondence. 282-289
  
• Anna M. Manning, David J. Haglin:
  A New Algorithm for Finding Minimal Sample Uniques for Use in Statistical Disclosure Assessment. 290-297
  
• Keith Marsolo, Srinivasan Parthasarathy:
  Alternate Representation of Distance Matrices for Characterization of Protein Structure. 298-305
  
• Shawn Martin:
  Training Support Vector Machines Using Gilbert's Algorithm. 306-313
  
• Steven Minton, Claude Nanjo, Craig A. Knoblock, Martin Michalowski, Matthew Michelson:
  A Heterogeneous Field Matching Method for Record Linkage. 314-321
  
• Jennifer Neville, David Jensen:
  Leveraging Relational Autocorrelation with Latent Group Models. 322-329
  
• Thomas Takeo Osugi, Kun Deng, Stephen D. Scott:
  Balancing Exploration and Exploitation: A New Algorithm for Active Machine Learning. 330-337
  
• Feng Pan, Wei Wang, Anthony K. H. Tung, Jiong Yang:
  Finding Representative Set from Massive Data. 338-345
  
• Spiros Papadimitriou, Aristides Gionis, Panayiotis Tsaparas, Risto A. Väisänen, Heikki Mannila, Christos Faloutsos:
  Parameter-Free Spatial Data Mining Using MDL. 346-353
  
• Panagiotis Papapetrou, George Kollios, Stan Sclaroff, Dimitrios Gunopulos:
  Discovering Frequent Arrangements of Temporal Intervals. 354-361
  
• Marcelino Pereira dos Santos Silva, Gilberto Câmara, Ricardo Cartaxo Modesto de Souza, Dalton M. Valeriano, Maria Isabel Sobral Escada:
  Mining Patterns of Change in Remote Sensing Image Databases. 362-369
  
• Saharon Rosset, Claudia Perlich, Bianca Zadrozny:
  Ranking-Based Evaluation of Regression Models. 370-377
  
• Lars Schmidt-Thieme:
  Compound Classification Models for Recommender Systems. 378-385
  
• Ted E. Senator:
  Multi-Stage Classification. 386-393
  
• Wing-Ho Shum, Kwong-Sak Leung, Man Leung Wong:
  Learning Functional Dependency Networks Based on Genetic Programming. 394-401
  
• Michael Steinbach, Vipin Kumar:
  Generalizing the Notion of Confidence. 402-409
  
• Jonathan Stoeckel, Glenn Fung:
  SVM Feature Selection for Classification of SPECT Images of Alzheimer's Disease Using Spatial Information. 410-417
  
• Jimeng Sun, Huiming Qu, Deepayan Chakrabarti, Christos Faloutsos:
  Neighborhood Formation and Anomaly Detection in Bipartite Graphs. 418-425
  
• Xingzhi Sun, Philip S. Yu:
  A Border-Based Approach for Hiding Sensitive Frequent Itemsets. 426-433
  
• Zoltán Szamonek, Csaba Szepesvári:
  X-mHMM: An Efficient Algorithm for Training Mixtures of HMMs When the Number of Mixtures Is Unknown. 434-441
  
• Raz Tamir:
  A Random Walk through Human Associations. 442-449
  
• Dacheng Tao, Xuelong Li, Weiming Hu, Stephen J. Maybank, Xindong Wu:
  Supervised Tensor Learning. 450-457
  
• Gang Wang, Hui Zhang, Zhihua Zhang, Frederick H. Lochovsky:
  A Bernoulli Relational Model for Nonlinear Embedding. 458-465
  
• Ke Wang, Benjamin C. M. Fung, Philip S. Yu:
  Template-Based Privacy Preservation in Classification Problems. 466-473
  
• Peng Wang, Haixun Wang, Xiaochen Wu, Wei Wang, Baile Shi:
  On Reducing Classifier Granularity in Mining Concept-Drifting Data Streams. 474-481
  
• Yongge Wang, Xintao Wu:
  Approximate Inverse Frequent Itemset Mining: Privacy, Complexity, and Approximation. 482-489
  
• Li Wei, Eamonn J. Keogh, Helga Van Herle, Agenor Mafra-Neto:
  Atomic Wedgie: Efficient Query Filtering for Streaming Times Series. 490-497
  
• Oksana Yakhnenko, Adrian Silvescu, Vasant Honavar:
  Discriminatively Trained Markov Model for Sequence Classification. 498-505
  
• Jie Yin, Qiang Yang:
  Integrating Hidden Markov Models and Spectral Analysis for Sensory Time Series Clustering. 506-513
  
• Peng Zhang, Norbert Riedel:
  Discriminant Analysis: A Unified Approach. 514-521
  
• Yi Zhang, W. Nick Street, Samuel Burer:
  Sharing Classifiers among Ensembles from Related Problem Domains. 522-529
  
• Kaidi Zhao, Bing Liu, Thomas M. Tirpak, Weimin Xiao:
  A Visual Data Mining Framework for Convenient Identification of Useful Knowledge. 530-537
  
• Dong Zhuang, Benyu Zhang, Qiang Yang, Jun Yan, Zheng Chen, Ying Chen:
  Efficient Text Classification by Weighted Proximal SVM. 538-545
  

Short Papers

• Hidenao Abe, Shusaku Tsumoto, Miho Ohsaki, Takahira Yamaguchi:
  A Rule Evaluation Support Method with Learning Models Based on Objective Rule Evaluation Indexes. 549-552
  
• Foto N. Afrati, Gautam Das, Aristides Gionis, Heikki Mannila, Taneli Mielikäinen, Panayiotis Tsaparas:
  Mining Chains of Relations. 553-556
  
• Fabio Aiolli:
  A Preference Model for Structured Supervised Learning Tasks. 557-560
  
• Maurizio Atzori, Francesco Bonchi, Fosca Giannotti, Dino Pedreschi:
  Blocking Anonymity Threats Raised by Frequent Itemset Mining. 561-564
  
• Abraham Bagherjeiran, Christoph F. Eick, Chun-Sheng Chen, Ricardo Vilalta:
  Adaptive Clustering: Obtaining Better Clusters Using Feedback and Past Experience. 565-568
  
• Jinbo Bi, Glenn Fung, Murat Dundar, R. Bharat Rao:
  Semi-Supervised Mixture of Kernels via LPBoost Methods. 569-572
  
• Haiyun Bian, Raj Bhatnagar:
  A Levelwise Search Algorithm for Interesting Subspace Clusters. 573-576
  
• Robin D. Burke, Bamshad Mobasher, Runa Bhaumik, Chad Williams:
  Segment-Based Injection Attacks against Collaborative Filtering Recommender Systems. 577-580
  
• Richard Butterworth, Gregory Piatetsky-Shapiro, Dan A. Simovici:
  On Feature Selection through Clustering. 581-584
  
• Gong Chen, Xindong Wu, Xingquan Zhu:
  Sequential Pattern Mining in Multiple Streams. 585-588
  
• Keke Chen, Ling Liu:
  Privacy Preserving Data Classification with Rotation Perturbation. 589-592
  
• Yixin Chen, Henry L. Bart Jr., Shuqing Huang, Huimin Chen:
  A Computational Framework for Taxonomic Research: Diagnosing Body Shape within Fish Species Complexes. 593-596
  
• Frans Coenen, Paul H. Leng:
  Obtaining Best Parameter Values for Accurate Classification. 597-600
  
• Federico Di Palma, Giuseppe De Nicolao, Guido Miraglia, Oliver M. Donzelli:
  Process Diagnosis via Electrical-Wafer-Sorting Maps Classification. 601-604
  
• Wei Fan, Ian Davidson, Bianca Zadrozny, Philip S. Yu:
  An Improved Categorization of Classifier's Sensitivity on Sample Selection Bias. 605-608
  
• Johannes Fischer, Volker Heun, Stefan Kramer:
  Fast Frequent String Mining Using Suffix Arrays. 609-612
  
• Ada Wai-Chee Fu, Raymond Chi-Wing Wong, Ke Wang:
  Privacy-Preserving Frequent Pattern Mining across Private Databases. 613-616
  
• Jie Gao, Jörg Denzinger, Robert C. James:
  CoLe: A Cooperative Data Mining Approach and Its Application to Early Diabetes Detection. 617-620
  
• Like Gao, Xiaoyang Sean Wang:
  Feature Selection for Building Cost-Effective Data Stream Classifiers. 621-624
  
• Thomas George, Srujana Merugu:
  A Scalable Collaborative Filtering Framework Based on Co-Clustering. 625-628
  
• Shantanu Godbole, Ganesh Ramakrishnan, Sunita Sarawagi:
  Text Classification with Evolving Label-Sets. 629-632
  
• Ningthoujam Gourakishwar Singh, Sanasam Ranbir Singh, Anjana K. Mahanta:
  CloseMiner: Discovering Frequent Closed Itemsets Using Frequent Closed Tidsets. 633-636
  
• Maria Halkidi, Dimitrios Gunopulos, Nitin Kumar, Michalis Vazirgiannis, Carlotta Domeniconi:
  A Framework for Semi-Supervised Learning Based on Subjective and Objective Clustering Criteria. 637-640
  
• Kirsten Hildrum, Philip S. Yu:
  Focused Community Discovery. 641-644
  
• Ayça Azgin Hintoglu, Ali Inan, Yücel Saygin, Mehmet Keskinöz:
  Suppressing Data Sets to Prevent Discovery of Association Rules. 645-648
  
• Han-Shen Huang, Bou-Ho Yang, Chun-Nan Hsu:
  Triple Jump Acceleration for the EM Algorithm. 649-652
  
• Jin Huang, Charles X. Ling:
  Partial Ensemble Classifiers Selection for Better Ranking. 653-656
  
• Tsuyoshi Idé:
  Pairwise Symmetry Decomposition Method for Generalized Covariance Analysis. 657-660
  
• Vandana Pursnani Janeja, Vijayalakshmi Atluri:
  FS³: A Random Walk Based Free-Form Spatial Scan Statistic for Anomalous Window Detection. 661-664
  
• Xing Jiang, Ah-Hwee Tan:
  Mining Ontological Knowledge from Domain-Specific Text Documents. 665-668
  
• Yuelong Jiang, Ke Wang, Alexander Tuzhilin, Ada Wai-Chee Fu:
  Mining Patterns That Respond to Actions. 669-672
  
• Toshihiro Kamishima, Hideto Kazawa, Shotaro Akaho:
  Supervised Ordering - An Empirical Survey. 673-676
  
• Ning Kang, Carlotta Domeniconi, Daniel Barbará:
  Categorization and Keyword Identification of Unlabeled Documents. 677-680
  
• Ismo Kärkkäinen, Pasi Fränti:
  Gradual Model Generator for Single-Pass Clustering. 681-684
  
• Paul Komarek, Andrew W. Moore:
  Making Logistic Regression a Core Data Mining Tool with TR-IRLS. 685-688
  
• Hans-Peter Kriegel, Martin Pfeifle:
  Hierarchical Density-Based Clustering of Uncertain Data. 689-692
  
• Nimit Kumar, Krishna Kummamuru, Deepa Paranjpe:
  Semi-Supervised Clustering with Metric Learning Using Relative Comparisons. 693-696
  
• Krishna Kummamuru, Raghu Krishnapuram, Rakesh Agrawal:
  On Learning Asymmetric Dissimilarity Measures. 697-700
  
• Longin Jan Latecki, Vasileios Megalooikonomou, Qiang Wang, Rolf Lakämper, Chotirat (Ann) Ratanamahatana, Eamonn J. Keogh:
  Partial Elastic Matching of Time Series. 701-704
  
• In-Yee Lee, Jan-Ming Ho, Ming-Syan Chen:
  CLUGO: A Clustering Algorithm for Automated Functional Annotations Based on Gene Ontology. 705-708
  
• Daniel Lemire, Martin Brooks, Yuhong Yan:
  An Optimal Linear Time Algorithm for Quasi-Monotonic Segmentation. 709-712
  
• Loïck Lhote, François Rioult, Arnaud Soulet:
  Average Number of Frequent (Closed) Patterns in Bernouilli and Markovian Databases. 713-716
  
• Charles X. Ling, Shengli Sheng, Tilmann F. W. Bruckhaus, Nazim H. Madhavji:
  Predicting Software Escalations with Maximum ROI. 717-720
  
• Jinze Liu, Susan Paulsen, Wei Wang, Andrew B. Nobel, Jan Prins:
  Mining Approximate Frequent Itemsets from Noisy Data. 721-724
  
• Ning Liu, Benyu Zhang, Jun Yan, Zheng Chen, Wenyin Liu, Fengshan Bai, Leefeng Chien:
  Text Representation: From Vector to Tensor. 725-728
  
• Elio Lozano, Edgar Acuña:
  Parallel Algorithms for Distance-Based and Density-Based Outliers. 729-732
  
• Tong Luo, Lawrence O. Hall, Dmitry B. Goldgof, Andrew Remsen:
  Bit Reduction Support Vector Machine. 733-736
  
• Sandeep Mane, Carson Murray, Shashi Shekhar, Jaideep Srivastava, Anne Pusey:
  Spatial Clustering of Chimpanzee Locations for Neighborhood Identification. 737-740
  
• Bruno Martins, Mário J. Silva:
  A Graph-Ranking Algorithm for Geo-Referencing Documents. 741-744
  
• Prem Melville, Foster J. Provost, Raymond J. Mooney:
  An Expected Utility Approach to Active Feature-Value Acquisition. 745-748
  
• Dheerendranath Mundluru, Jayasimha Reddy Katukuri, Saygin Celebi:
  Automatically Mining Result Records from Search Engine Response Pages. 749-752
  
• Jian Pei, Jian Liu, Haixun Wang, Ke Wang, Philip S. Yu, Jianyong Wang:
  Efficiently Mining Frequent Closed Partial Orders. 753-756
  
• Kunal Punera, Joydeep Ghosh:
  CLUMP: A Scalable and Robust Framework for Structure Discovery. 757-760
  
• Martin Scholz:
  On the Tractability of Rule Discovery from Distributed Data. 761-764
  
• Jiazheng Shi, Ashok Samal, David Marx:
  Face Recognition Using Landmark-Based Bidimensional Regression. 765-768
  
• Arno Siebes, Muhammad Subianto, A. J. Feelders:
  Instability of Classifiers on Categorical Data. 769-772
  
• Lisa Singh, Lise Getoor, Louis Licamele:
  Pruning Social Networks Using Structural Properties and Descriptive Attributes. 773-776
  
• Arnaud Soulet, Bruno Crémilleux:
  Optimizing Constraint-Based Mining by Automatically Relaxing Constraints. 777-780
  
• Lei Tang, Huan Liu:
  Bias Analysis in Text Classification for Highly Skewed Data. 781-784
  
• Alexandre Termier, Marie-Christine Rousset, Michèle Sebag, Kouzou Ohara, Takashi Washio, Hiroshi Motoda:
  Efficient Mining of High Branching Factor Attribute Trees. 785-788
  
• Chi-Ho Tsang, Sam Kwong, Hanli Wang:
  Anomaly Intrusion Detection Using Multi-Objective Genetic Fuzzy System and Agent-Based Evolutionary Computation Framework. 789-792
  
• Takashi Washio, Yuki Mitsunaga, Hiroshi Motoda:
  Mining Quantitative Frequent Itemsets Using Adaptive Density-Based Subspace Clustering. 793-796
  
• Tak-Lam Wong, Wai Lam:
  Hot Item Mining and Summarization from Multiple Auction Web Sites. 797-800
  
• Wensheng Wu, AnHai Doan, Clement T. Yu:
  Merging Interface Schemas on the Deep Web via Clustering Aggregation. 801-804
  
• Kiyoung Yang, Cyrus Shahabi:
  On the Stationarity of Multivariate Time Series for Correlation-Based Data Analysis. 805-808
  
• Sandeep Yaramakala, Dimitris Margaritis:
  Speculative Markov Blanket Discovery for Optimal Feature Selection. 809-812
  
• Jin Soung Yoo, Shashi Shekhar, Mete Celik:
  A Join-Less Approach for Co-Location Pattern Mining: A Summary of Results. 813-816
  
• Kun Zhang, Zujia Xu, Jing Peng, Bill P. Buckles:
  Learning through Changes: An Empirical Study of Dynamic Behaviors of Probability Estimation Trees. 817-820
  
• Xiaofeng Zhang, William K. Cheung:
  Visualizing Global Manifold Based on Distributed Local Data Abstractions. 821-824
  
• Yi Zhang, W. Nick Street:
  Bagging with Adaptive Costs. 825-828
  
• Cui Zhu, Hiroyuki Kitagawa, Christos Faloutsos:
  Example-Based Robust Outlier Detection in High Dimensional Datasets. 829-832
  
• Albrecht Zimmermann, Björn Bringmann:
  CTC - Correlating Tree Patterns for Classification. 833-836