Table of Contents

• On certain rigorous approaches to data mining (invited talk, abstract only)Christos H. Papadimitriou. 2 [doi]
• Informed knowledge discovery: using prior knowledge in discovery programs (invited talk, abstract only)Bruce G. Buchanan. 3 [doi]
• Among those dark electronic mills: privacy and data mining (invited talk, abstract only)Jason Catlett. 4 [doi]
• Decision support in the booming e-world (invited talk, abstract only)James Goodnight. 5 [doi]
• Hancock: a language for extracting signatures from data streamsCorinna Cortes, Kathleen Fisher, Daryl Pregibon, Anne Rogers. 9-17 [doi]
• Mining IC test data to optimize VLSI testingTony Fountain, Thomas G. Dietterich, Bill Sudyka. 18-25 [doi]
• An empirical analysis of techniques for constructing and searching k-dimensional treesDouglas A. Talbert, Douglas H. Fisher. 26-33 [doi]
• Generating non-redundant association rulesMohammed Javeed Zaki. 34-43 [doi]
• 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 (RADS)Ted E. Senator. 44-53 [doi]
• Small is beautiful: discovering the minimal set of unexpected patternsBalaji Padmanabhan, Alexander Tuzhilin. 54-63 [doi]
• Data selection for support vector machine classifiersGlenn Fung, Olvi L. Mangasarian. 64-70 [doi]
• Mining high-speed data streamsPedro Domingos, Geoff Hulten. 71-80 [doi]
• Deformable Markov model templates for time-series pattern matchingXianping Ge, Padhraic Smyth. 81-90 [doi]
• Active learning using adaptive resamplingVijay S. Iyengar, Chidanand Apté, Tong Zhang. 91-98 [doi]
• Efficient search for association rulesGeoffrey I. Webb. 99-107 [doi]
• Depth first generation of long patternsRamesh C. Agarwal, Charu C. Aggarwal, V. V. V. Prasad. 108-118 [doi]
• The IGrid index: reversing the dimensionality curse for similarity indexing in high dimensional spaceCharu C. Aggarwal, Philip S. Yu. 119-129 [doi]
• The generalized Bayesian committee machineVolker Tresp. 130-139 [doi]
• A general probabilistic framework for clustering individuals and objectsIgor V. Cadez, Scott Gaffney, Padhraic Smyth. 140-149 [doi]
• Efficient identification of Web communitiesGary William Flake, Steve Lawrence, C. Lee Giles. 150-160 [doi]
• Global partial orders from sequential dataHeikki Mannila, Christopher Meek. 161-168 [doi]
• Towards an effective cooperation of the user and the computer for classificationMihael Ankerst, Martin Ester, Hans-Peter Kriegel. 179-188 [doi]
• Explicitly representing expected cost: an alternative to ROC representationChris Drummond, Robert C. Holte. 198-207 [doi]
• Multi-level organization and summarization of the discovered rulesBing Liu, Minqing Hu, Wynne Hsu. 208-217 [doi]
• Visualization and the process of modeling: a cognitive-theoretic viewAndrew W. Crapo, Laurie B. Waisel, William A. Wallace, Thomas R. Willemain. 218-226 [doi]
• Visualizing association rules with interactive mosaic plotsHeike Hofmann, Arno Siebes, Adalbert F. X. Wilhelm. 227-235 [doi]
• ::::RuleViz::::: a model for visualizing knowledge discovery processJianchao Han, Nick Cercone. 244-253 [doi]
• Hardening soft information sourcesWilliam W. Cohen, Henry A. Kautz, David A. McAllester. 255-259 [doi]
• Using the fractal dimension to cluster datasetsDaniel Barbará, Ping Chen. 260-264 [doi]
• Growing decision trees on support-less association rulesKe Wang, Senqiang Zhou, Yu He. 265-269 [doi]
• Efficient mining of weighted association rules (WAR)Wei Wang, Jiong Yang, Philip S. Yu. 270-274 [doi]
• Mining asynchronous periodic patterns in time series dataJiong Yang, Wei Wang 0010, Philip S. Yu. 275-279 [doi]
• Visualization of navigation patterns on a Web site using model-based clusteringIgor V. Cadez, David Heckerman, Christopher Meek, Padhraic Smyth, Steven White. 280-284 [doi]
• Scaling up dynamic time warping for datamining applicationsEamonn J. Keogh, Michael J. Pazzani. 285-289 [doi]
• IntelliClean: a knowledge-based intelligent data cleanerMong-Li Lee, Tok Wang Ling, Wai Lup Low. 290-294 [doi]
• Towards scalable support vector machines using squashingDmitry Pavlov, Darya Chudova, Padhraic Smyth. 295-299 [doi]
• A data mining framework for optimal product selection in retail supermarket data: the generalized PROFSET modelTom Brijs, Bart Goethals, Gilbert Swinnen, Koen Vanhoof, Geert Wets. 300-304 [doi]
• Application of neural networks to biological data mining: a case study in protein sequence classificationJason Tsong-Li Wang, Qicheng Ma, Dennis Shasha, Cathy H. Wu. 305-309 [doi]
• Exploring constraints to efficiently mine emerging patterns from large high-dimensional datasetsXiuzhen Zhang, Guozhu Dong, Kotagiri Ramamohanarao. 310-314 [doi]
• Multivariate discretization of continuous variables for set miningStephen D. Bay. 315-319 [doi]
• On-line unsupervised outlier detection using finite mixtures with discounting learning algorithmsKenji Yamanishi, Jun-ichi Takeuchi, Graham J. Williams, Peter Milne. 320-324 [doi]
• Unsupervised Bayesian visualization of high-dimensional dataPetri Kontkanen, Jussi Lahtinen, Petri Myllymäki, Henry Tirri. 325-329 [doi]
• A sequential sampling algorithm for a general class of utility criteriaTobias Scheffer, Stefan Wrobel. 330-334 [doi]
• Efficient algorithms for constructing decision trees with constraintsMinos N. Garofalakis, Dongjoon Hyun, Rajeev Rastogi, Kyuseok Shim. 335-339 [doi]
• A classifier for semi-structured documentsJeonghee Yi, Neel Sundaresan. 340-344 [doi]
• Alpha seeding for support vector machinesDennis DeCoste, Kiri Wagstaff. 345-349 [doi]
• Can we push more constraints into frequent pattern mining?Jian Pei, Jiawei Han. 350-354 [doi]
• FreeSpan: frequent pattern-projected sequential pattern miningJiawei Han, Jian Pei, Behzad Mortazavi-Asl, Qiming Chen, Umeshwar Dayal, Meichun Hsu. 355-359 [doi]
• Visualization and interactive feature selection for unsupervised dataJennifer G. Dy, Carla E. Brodley. 360-364 [doi]
• Feature selection in unsupervised learning via evolutionary searchYongSeog Kim, W. Nick Street, Filippo Menczer. 365-369 [doi]
• Classification and visualization for high-dimensional dataAlfred Inselberg, Tova Avidan. 370-374 [doi]
• Data mining solves tough semiconductor manufacturing problemsMike Gardner, Jack Bieker. 376-383 [doi]
• Genome scale prediction of protein functional class from sequence using data miningRoss D. King, Andreas Karwath, Amanda Clare, Luc Dehaspe. 384-389 [doi]
• Data mining to detect abnormal behavior in aerospace dataJosé Manuel Peña, Fazel Famili, Sylvain Létourneau. 390-397 [doi]
• Predictive modeling in automotive direct marketing: tools, experiences and open issuesWendy Gersten, Rüdiger Wirth, Dirk Arndt. 398-406 [doi]
• Agglomerative clustering of a search engine query logDoug Beeferman, Adam L. Berger. 407-416 [doi]
• Textual data mining of service center call recordsPang-Ning Tan, Hannah Blau, Steven A. Harp, Robert P. Goldman. 417-423 [doi]
• Automating exploratory data analysis for efficient data miningJonathan D. Becher, Pavel Berkhin, Edmund Freeman. 424-429 [doi]
• Exploration mining in diabetic patients databases: findings and conclusionsWynne Hsu, Mong-Li Lee, Bing Liu, Tok Wang Ling. 430-436 [doi]
• Cross-sell: a fast promotion-tunable customer-item recommendation method based on conditionally independent probabilitiesBrendan Kitts, David Freed, Martin Vrieze. 437-446 [doi]
• Identifying prospective customersPaul B. Chou, Edna Grossman, Dimitrios Gunopulos, Pasumarti Kamesam. 447-456 [doi]
• Targeting the right students using data miningYiming Ma, Bing Liu, Ching Kian Wong, Philip S. Yu, Shuik Ming Lee. 457-464 [doi]
• Evolutionary algorithms in data mining: multi-objective performance modeling for direct marketingSiddhartha Bhattacharyya. 465-473 [doi]
• Hybrid Poisson processAyman Farahat. 474-479 [doi]
• Data mining techniques for optimizing inventories for electronic commerceAnjali Dhond, Amar Gupta, Sanjeev Vadhavkar. 480-486 [doi]
• Mining the stock market (extended abstract): which measure is best?Martin Gavrilov, Dragomir Anguelov, Piotr Indyk, Rajeev Motwani. 487-496 [doi]
• Discovering similar patterns in time seriesJuan Pedro Caraça-Valente, Ignacio Lopez-Chavarrias. 497-505 [doi]
• Defection detection: using activity profiles to predict ISP customer vulnerabilityNandini Raghavan, Robert M. Bell, Matthias Schonlau. 506-515 [doi]
• Incremental quantile estimation for massive trackingFei Chen, Diane Lambert, José C. Pinheiro. 516-522 [doi]
• Discovery of multi-level rules and exceptions from a distributed databaseRónán Páircéir, Sally I. McClean, Bryan W. Scotney. 523-532 [doi]
• Text mining (workshop session - title only)Marko Grobelnik, Dunja Mladenic, Natasa Milic-Frayling. 534 [doi]
• Distributed and parallel knowledge discovery (workshop session - title only)Hillol Kargupta, Philip Chan, Vipin Kumar, Zoran Obradovic. 534 [doi]
• Web mining for e-commerce (workshop session - title only)Ron Kohavi, Myra Spiliopoulou, Jaideep Srivastava. 534 [doi]