Table of Contents

• Value-based data mining and web mining for CRMSteve Gallant, Gregory Piatetsky-Shapiro, Ming Tan. [doi]
• E-business enterprise data miningUsama M. Fayyad, Neal Rothleder, Paul S. Bradley. [doi]
• Scalable frequent-pattern mining methods: an overviewJiawei Han, Laks V. S. Lakshmanan, Jian Pei. [doi]
• Data mining for outliers with robust statisticsR. Douglas Martin. [doi]
• Data mining to go : ubiquitous KDD for mobile and distributed environmentsHillol Kargupta, Anupam Joshi. [doi]
• Advances in decision tree constructionJohannes Gehrke, Wei-Yin Loh. [doi]
• Determination of RNA folding pathway functional intermediates using a massively parallel genetic algorithm (abstract of invited talk)Bruce A. Shapiro, David Bengali, Wojciech Kasprzak. 1 [doi]
• Extracting knowledge from gene expression data: A case study of Batten DiseaseSimon M. Lin, Sumeer Dhar, Rose-Mary N. Boustany. 2-7 [doi]
• Challenges for knowledge discovery in biologyRuss B. Altman. 2 [doi]
• Mass collaboration and data miningRaghu Ramakrishnan. 4 [doi]
• Mining e-commerce data: the good, the bad, and the uglyRon Kohavi. 8-13 [doi]
• Data mining platform for database developersAmir Netz. 14 [doi]
• Classification of genes using probabilistic models of microarray expression profilesPaul Pavlidis, Christopher Tang, William Stafford Noble. 15-21 [doi]
• The DGX distribution for mining massive, skewed dataZhiqiang Bi, Christos Faloutsos, Flip Korn. 17-26 [doi]
• Analysis of an associative memory neural network for pattern identification in gene expression dataSilvio Bicciato, Mario Pandin, Giuseppe Didone, Carlo Di Bello. 22-30 [doi]
• Data mining criteria for tree-based regression and classificationAndreas Buja, Yung-Seop Lee. 27-36 [doi]
• A learning algorithm for string assemblyMark K. Goldberg, Darren T. Lim, Malik Magdon-Ismail. 31-37 [doi]
• Probabilistic modeling of transaction data with applications to profiling, visualization, and predictionIgor V. Cadez, Padhraic Smyth, Heikki Mannila. 37-46 [doi]
• A probabilistic approach to sequence assembly validationSun Kim, Li Liao, Jean-Francois Tomb. 38-43 [doi]
• Shared challenges in data mining and computational biology (abstract of invited talk)Charles Elkan. 44 [doi]
• Learning to recognize brain specific proteins based on low-level features from on-line prediction serversMikael Huss, Henrik Boström, Lars Asker, Joakim Cöster. 45-49 [doi]
• GESS: a scalable similarity-join algorithm for mining large data sets in high dimensional spacesJens-Peter Dittrich, Bernhard Seeger. 47-56 [doi]
• Investigation of bagging-like effects and decision trees versus neural nets in protein secondary structure predictionNitesh V. Chawla, Thomas E. Moore, Kevin W. Bowyer, Lawrence O. Hall, Clayton Springer, W. Philip Kegelmeyer. 50-59 [doi]
• Mining the network value of customersPedro Domingos, Matthew Richardson. 57-66 [doi]
• Maximum entropy methods for biological sequence modelingEugen C. Buehler, Lyle H. Ungar. 60-64 [doi]
• Hierarchical cluster analysis of SAGE data for cancer profilingRaymond T. Ng, Jörg Sander, Monica C. Sleumer. 65-72 [doi]
• Empirical bayes screening for multi-item associationsWilliam DuMouchel, Daryl Pregibon. 67-76 [doi]
• A scalable algorithm for clustering protein sequencesValerie Guralnik, George Karypis. 73-80 [doi]
• Proximal support vector machine classifiersGlenn Fung, Olvi L. Mangasarian. 77-86 [doi]
• Data mining with sparse grids using simplicial basis functionsJochen Garcke, Michael Griebel. 87-96 [doi]
• Mining time-changing data streamsGeoff Hulten, Laurie Spencer, Pedro Domingos. 97-106 [doi]
• Visualizing multi-dimensional clusters, trends, and outliers using star coordinatesEser Kandogan. 107-116 [doi]
• Ensemble-index: a new approach to indexing large databasesEamonn J. Keogh, Selina Chu, Michael J. Pazzani. 117-125 [doi]
• Robust space transformations for distance-based operationsEdwin M. Knorr, Raymond T. Ng, Ruben H. Zamar. 126-135 [doi]
• Molecular feature mining in HIV dataStefan Kramer, Luc De Raedt, Christoph Helma. 136-143 [doi]
• Discovering unexpected information from your competitors web sitesBing Liu, Yiming Ma, Philip S. Yu. 144-153 [doi]
• Personalization from incomplete data: what you don t know can hurtBalaji Padmanabhan, Zhiqiang Zheng, Steven Orla Kimbrough. 154-163 [doi]
• Probabilistic query models for transaction dataDmitry Pavlov, Padhraic Smyth. 164-173 [doi]
• Extracting collective probabilistic forecasts from web gamesDavid M. Pennock, Steve Lawrence, Finn Årup Nielsen, C. Lee Giles. 174-183 [doi]
• Tri-plots: scalable tools for multidimensional data miningAgma J. M. Traina, Caetano Traina Jr., Spiros Papadimitriou, Christos Faloutsos. 184-193 [doi]
• Efficient discovery of error-tolerant frequent itemsets in high dimensionsCheng Yang, Usama M. Fayyad, Paul S. Bradley. 194-203 [doi]
• Learning and making decisions when costs and probabilities are both unknownBianca Zadrozny, Charles Elkan. 204-213 [doi]
• Data mining case study: modeling the behavior of offenders who commit serious sexual assaultsRichard Adderley, Peter B. Musgrove. 215-220 [doi]
• A human-computer cooperative system for effective high dimensional clusteringCharu C. Aggarwal. 221-226 [doi]
• Mining massively incomplete data sets by conceptual reconstructionCharu C. Aggarwal, Srinivasan Parthasarathy. 227-232 [doi]
• Evaluating the novelty of text-mined rules using lexical knowledgeSugato Basu, Raymond J. Mooney, Krupakar V. Pasupuleti, Joydeep Ghosh. 233-238 [doi]
• Fast ordering of large categorical datasets for better visualizationAlina Beygelzimer, Chang-Shing Perng, Sheng Ma. 239-244 [doi]
• Random projection in dimensionality reduction: applications to image and text dataElla Bingham, Heikki Mannila. 245-250 [doi]
• PVA: a self-adaptive personal view agent systemChien Chin Chen, Meng Chang Chen, Yeali S. Sun. 257-262 [doi]
• A robust and scalable clustering algorithm for mixed type attributes in large database environmentTom Chiu, DongPing Fang, John Chen, Yao Wang, Christopher Jeris. 263-268 [doi]
• Co-clustering documents and words using bipartite spectral graph partitioningInderjit S. Dhillon. 269-274 [doi]
• A spectral method to separate disconnected and nearly-disconnected web graph componentsChris H. Q. Ding, Xiaofeng He, Hongyuan Zha. 275-280 [doi]
• Clustering spatial data using random walksDavid Harel, Yehuda Koren. 281-286 [doi]
• Solving regression problems with rule-based ensemble classifiersNitin Indurkhya, Sholom M. Weiss. 287-292 [doi]
• Mining top-n local outliers in large databasesWen Jin, Anthony K. H. Tung, Jiawei Han. 293-298 [doi]
• Generalized clustering, supervised learning, and data assignmentAnnaka Kalton, Pat Langley, Kiri Wagstaff, Jungsoon P. Yoo. 299-304 [doi]
• Mining a stream of transactions for customer patternsDiane Lambert, José C. Pinheiro. 305-310 [doi]
• The distributed boosting algorithmAleksandar Lazarevic, Zoran Obradovic. 311-316 [doi]
• DIRT @SBT@discovery of inference rules from textDekang Lin, Patrick Pantel. 323-328 [doi]
• Identifying non-actionable association rulesBing Liu, Wynne Hsu, Yiming Ma. 329-334 [doi]
• Discovering the set of fundamental rule changesBing Liu, Wynne Hsu, Yiming Ma. 335-340 [doi]
• Finding simple intensity descriptions from event sequence dataHeikki Mannila, Marko Salmenkivi. 341-346 [doi]
• Data filtering for automatic classification of rocks from reflectance spectraJonathan Moody, Ricardo Bezerra de Andrade e Silva, Joseph Vanderwaart. 347-352 [doi]
• Mining frequent neighboring class sets in spatial databasesYasuhiko Morimoto. 353-358 [doi]
• Experimental comparisons of online and batch versions of bagging and boostingNikunj C. Oza, Stuart J. Russell. 359-364 [doi]
• TreeDT: gene mapping by tree disequilibrium testPetteri Sevon, Hannu Toivonen, Vesa Ollikainen. 365-370 [doi]
• Detecting graph-based spatial outliers: algorithms and applications (a summary of results)Shashi Shekhar, Chang-Tien Lu, Pusheng Zhang. 371-376 [doi]
• A streaming ensemble algorithm (SEA) for large-scale classificationW. Nick Street, YongSeog Kim. 377-382 [doi]
• Discovering associations with numeric variablesGeoffrey I. Webb. 383-388 [doi]
• Discovering outlier filtering rules from unlabeled data: combining a supervised learner with an unsupervised learnerKenji Yamanishi, Jun-ichi Takeuchi. 389-394 [doi]
• Infominer: mining surprising periodic patternsJiong Yang, Wei Wang 0010, Philip S. Yu. 395-400 [doi]
• Real world performance of association rule algorithmsZijian Zheng, Ron Kohavi, Llew Mason. 401-406 [doi]
• Segmentation-based modeling for advanced targeted marketingChidanand Apté, E. Bibelnieks, Ramesh Natarajan, Edwin P. D. Pednault, Fateh Tipu, Deb Campbell, Bryan Nelson. 408-413 [doi]
• Interactive path analysis of web site traffic414-419 [doi]
• Estimating business targetsPiew Datta, James Drew, Andrew Betz, D. R. Mani, Jeffery Howard. 420-425 [doi]
• Magical thinking in data mining: lessons from CoIL challenge 2000Charles Elkan. 426-431 [doi]
• REVI-MINER, a KDD-environment for deviation detection and analysis of warranty and goodwill cost statements in automotive industryEdgar Hotz, Udo Grimmer, W. Heuser, Gholamreza Nakhaeizadeh, M. Wieczorek. 432-437 [doi]
• Mining from open answers in questionnaire dataHang Li, Kenji Yamanishi. 443-449 [doi]
• Funnel report mining for the MSN networkTeresa Mah, Hank Hoek, Ying Li. 450-455 [doi]
• Evaluation of prediction models for marketing campaignsSaharon Rosset, Einat Neumann, Uri Eick, Nurit Vatnik, Yizhak Idan. 456-461 [doi]
• Knowledge base maintenance using knowledge gap analysisW. Scott Spangler, Jeffrey T. Kreulen. 462-466 [doi]
• Mining user session data to facilitate user interaction with a customer service knowledge base in RightNow WebDoug Warner, J. Neal Richter, Stephen D. Durbin, Bikramjit Banerjee. 467-472 [doi]
• Mining web logs for prediction models in WWW caching and prefetchingQiang Yang, Henry Haining Zhang, Ian Tian Yi Li. 473-478 [doi]