Table of Contents

• Customer lifetime value modeling and its use for customer retention planning [doi]
• Bayesian analysis of massive datasets via particle filtersGreg Ridgeway, David Madigan. 5-13 [doi]
• Scalable robust covariance and correlation estimates for data miningFatemah A. Alqallaf, Kjell P. Konis, R. Douglas Martin, Ruben H. Zamar. 14-23 [doi]
• MARK: a boosting algorithm for heterogeneous kernel modelsKristin P. Bennett, Michinari Momma, Mark J. Embrechts. 24-31 [doi]
• Selecting the right interestingness measure for association patternsPang-Ning Tan, Vipin Kumar, Jaideep Srivastava. 32-41 [doi]
• DualMiner: a dual-pruning algorithm for itemsets with constraintsCristian Bucila, Johannes Gehrke, Daniel Kifer, Walker M. White. 42-51 [doi]
• Querying multiple sets of discovered rulesAlexander Tuzhilin, Bing Liu. 52-60 [doi]
• Mining knowledge-sharing sites for viral marketingMatthew Richardson, Pedro Domingos. 61-70 [doi]
• Efficiently mining frequent trees in a forestMohammed Javeed Zaki. 71-80 [doi]
• ANF: a fast and scalable tool for data mining in massive graphsChristopher R. Palmer, Phillip B. Gibbons, Christos Faloutsos. 81-90 [doi]
• Bursty and hierarchical structure in streamsJon M. Kleinberg. 91-101 [doi]
• On the need for time series data mining benchmarks: a survey and empirical demonstrationEamonn J. Keogh, Shruti Kasetty. 102-111 [doi]
• Query, analysis, and visualization of hierarchically structured data using PolarisChris Stolte, Diane Tang, Pat Hanrahan. 112-122 [doi]
• On interactive visualization of high-dimensional data using the hyperbolic planeJörg A. Walter, Helge Ritter. 123-132 [doi]
• Optimizing search engines using clickthrough dataThorsten Joachims. 133-142 [doi]
• Relational Markov models and their application to adaptive web navigationCorin R. Anderson, Pedro Domingos, Daniel S. Weld. 143-152 [doi]
• Pattern discovery in sequences under a Markov assumptionDarya Chudova, Padhraic Smyth. 153-162 [doi]
• On effective classification of strings with waveletsCharu C. Aggarwal. 163-172 [doi]
• Shrinkage estimator generalizations of Proximal Support Vector MachinesDeepak K. Agarwal. 173-182 [doi]
• Hierarchical model-based clustering of large datasets through fractionation and refractionationJeremy Tantrum, Alejandro Murua, Werner Stuetzle. 183-190 [doi]
• Enhanced word clustering for hierarchical text classificationInderjit S. Dhillon, Subramanyam Mallela, Rahul Kumar. 191-200 [doi]
• A parallel learning algorithm for text classificationCanasai Kruengkrai, Chuleerat Jaruskulchai. 201-206 [doi]
• A refinement approach to handling model misfit in text categorizationHaoran Wu, Tong-Heng Phang, Bing Liu, Xiaoli Li. 207-216 [doi]
• Privacy preserving mining of association rulesAlexandre V. Evfimievski, Ramakrishnan Srikant, Rakesh Agrawal, Johannes Gehrke. 217-228 [doi]
• Mining frequent item sets by opportunistic projectionJunqiang Liu, Yunhe Pan, Ke Wang, Jiawei Han. 229-238 [doi]
• PEBL: positive example based learning for Web page classification using SVMHwanjo Yu, Jiawei Han, Kevin Chen-Chuan Chang. 239-248 [doi]
• Web site mining: a new way to spot competitors, customers and suppliers in the world wide webMartin Ester, Hans-Peter Kriegel, Matthias Schubert. 249-258 [doi]
• Sequential cost-sensitive decision making with reinforcement learningEdwin P. D. Pednault, Naoki Abe, Bianca Zadrozny. 259-268 [doi]
• Interactive deduplication using active learningSunita Sarawagi, Anuradha Bhamidipaty. 269-278 [doi]
• Transforming data to satisfy privacy constraintsVijay S. Iyengar. 279-288 [doi]
• Exploiting unlabeled data in ensemble methodsKristin P. Bennett, Ayhan Demiriz, Richard Maclin. 289-296 [doi]
• Predicting rare classes: can boosting make any weak learner strong?Mahesh V. Joshi, Ramesh C. Agarwal, Vipin Kumar. 297-306 [doi]
• Efficient handling of high-dimensional feature spaces by randomized classifier ensemblesAleksander Kolcz, Xiaomei Sun, Jugal K. Kalita. 307-313 [doi]
• From run-time behavior to usage scenarios: an interaction-pattern mining approachMohammad El-Ramly, Eleni Stroulia, Paul G. Sorenson. 315-324 [doi]
• Exploiting response models: optimizing cross-sell and up-sell opportunities in bankingAndrew Storey, Marc-David Cohen. 325-331 [doi]
• Customer lifetime value modeling and its use for customer retention planningSaharon Rosset, Einat Neumann, Uri Eick, Nurit Vatnik, Yizhak Idan. 332-340 [doi]
• Mining product reputations on the WebSatoshi Morinaga, Kenji Yamanishi, Kenji Tateishi, Toshikazu Fukushima. 341-349 [doi]
• Learning domain-independent string transformation weights for high accuracy object identificationSheila Tejada, Craig A. Knoblock, Steven Minton. 350-359 [doi]
• A system for real-time competitive market intelligenceSholom M. Weiss, Naval K. Verma. 360-365 [doi]
• Mining intrusion detection alarms for actionable knowledgeKlaus Julisch, Marc Dacier. 366-375 [doi]
• Learning nonstationary models of normal network traffic for detecting novel attacksMatthew V. Mahoney, Philip K. Chan. 376-385 [doi]
• ADMIT: anomaly-based data mining for intrusionsKarlton Sequeira, Mohammed Javeed Zaki. 386-395 [doi]
• Handling very large numbers of association rules in the analysis of microarray dataAlexander Tuzhilin, Gediminas Adomavicius. 396-404 [doi]
• On the potential of domain literature for clustering and Bayesian network learningPeter Antal, Patrick Glenisson, Geert Fannes. 405-414 [doi]
• Mining heterogeneous gene expression data with time lagged recurrent neural networksYulan Liang, Arpad Kelemen. 415-421 [doi]
• Collaborative crawling: mining user experiences for topical resource discoveryCharu C. Aggarwal. 423-428 [doi]
• Sequential PAttern mining using a bitmap representationJay Ayres, Jason Flannick, Johannes Gehrke, Tomi Yiu. 429-435 [doi]
• Frequent term-based text clusteringFlorian Beil, Martin Ester, Xiaowei Xu. 436-442 [doi]
• A theoretical framework for learning from a pool of disparate data sourcesShai Ben-David, Johannes Gehrke, Reba Schuller. 443-449 [doi]
• Topics in 0--1 dataElla Bingham, Heikki Mannila, Jouni K. Seppänen. 450-455 [doi]
• Extracting decision trees from trained neural networksOlcay Boz. 456-461 [doi]
• A new two-phase sampling based algorithm for discovering association rulesBin Chen, Peter J. Haas, Peter Scheuermann. 462-468 [doi]
• CVS: a Correlation-Verification based Smoothing technique on information retrieval and term clusteringChristina Yip Chung, Bin Chen. 469-474 [doi]
• Learning to match and cluster large high-dimensional data sets for data integrationWilliam W. Cohen, Jacob Richman. 475-480 [doi]
• SECRET: a scalable linear regression tree algorithmAlin Dobra, Johannes Gehrke. 481-487 [doi]
• Tina Eliassi-Rad, Terence Critchlow, Ghaleb AbdullaTina Eliassi-Rad, Terence Critchlow, Ghaleb Abdulla. 488-494 [doi]
• Tumor cell identification using features rulesBin Fang, Wynne Hsu, Mong-Li Lee. 495-500 [doi]
• Integrating feature and instance selection for text classificationDimitris Fragoudis, Dimitris Meretakis, Spiros Likothanassis. 501-506 [doi]
• SyMP: an efficient clustering approach to identify clusters of arbitrary shapes in large data setsHichem Frigui. 507-512 [doi]
• Scaling multi-class support vector machines using inter-class confusionShantanu Godbole, Sunita Sarawagi, Soumen Chakrabarti. 513-518 [doi]
• Visualization support for a user-centered KDD processTu Bao Ho, Trong Dung Nguyen, DucDung Nguyen. 519-524 [doi]
• Mining complex models from arbitrarily large databases in constant timeGeoff Hulten, Pedro Domingos. 525-531 [doi]
• A model for discovering customer value for E-contentSrinivasan Jagannathan, Jayanth Nayak, Kevin C. Almeroth, Markus Hofmann. 532-537 [doi]
• SimRank: a measure of structural-context similarityGlen Jeh, Jennifer Widom. 538-543 [doi]
• Similarity measure based on partial information of time seriesXiaoming Jin, Yuchang Lu, Chunyi Shi. 544-549 [doi]
• Finding surprising patterns in a time series database in linear time and spaceEamonn J. Keogh, Stefano Lonardi, Bill Yuan-chi Chiu. 550-556 [doi]
• Clustering seasonality patterns in the presence of errorsMahesh Kumar, Nitin R. Patel, Jonathan Woo. 557-563 [doi]
• Construct robust rule sets for classificationJiuyong Li, Rodney W. Topor, Hong Shen. 564-569 [doi]
• Instability of decision tree classification algorithmsRuey-Hsia Li, Geneva G. Belford. 570-575 [doi]
• Distributed data mining in a chain store database of short transactionsCheng-Ru Lin, Chang-Hung Lee, Ming-Syan Chen, Philip S. Yu. 576-581 [doi]
• A robust and efficient clustering algorithm based on cohesion self-mergingCheng-Ru Lin, Ming-Syan Chen. 582-587 [doi]
• Discovering informative content blocks from Web documentsShian-Hua Lin, Jan-Ming Ho. 588-593 [doi]
• Collusion in the U.S. crop insurance program: applied data miningBertis B. Little, Walter L. Johnston, Ashley C. Lovell, Roderick M. Rejesus, Steve A. Steed. 594-598 [doi]
• Incremental context mining for adaptive document classificationRey-Long Liu, Yun-Ling Lu. 599-604 [doi]
• Evaluating classifiers performance in a constrained environmentAnna Olecka. 605-612 [doi]
• Discovering word senses from textPatrick Pantel, Dekang Lin. 613-619 [doi]
• Combining clustering and co-training to enhance text classification using unlabelled dataBhavani Raskutti, Herman L. Ferrá, Adam Kowalczyk. 620-625 [doi]
• Single-shot detection of multiple categories of text using parametric mixture modelsNaonori Ueda, Kazumi Saito. 626-631 [doi]
• Privacy preserving association rule mining in vertically partitioned dataJaideep Vaidya, Chris Clifton. 639-644 [doi]
• What s the code?: automatic classification of source code archivesSecil Ugurel, Robert Krovetz, C. Lee Giles. 639-644 [doi]
• Non-linear dimensionality reduction techniques for classification and visualizationMichail Vlachos, Carlotta Domeniconi, Dimitrios Gunopulos, George Kollios, Nick Koudas. 645-651 [doi]
• Item selection by hub-authority profit rankingKe Wang, Ming-Yen Thomas Su. 652-657 [doi]
• Discovery net: towards a grid of knowledge discoveryVasa Curcin, Moustafa Ghanem, Yike Guo, Martin Köhler, Anthony Rowe, Jameel Syed, Patrick Wendel. 658-663 [doi]
• Making every bit count: fast nonlinear axis scalingLeejay Wu, Christos Faloutsos. 664-669 [doi]
• B-EM: a classifier incorporating bootstrap with EM approach for data miningXintao Wu, Jianping Fan, Kalpathi R. Subramanian. 670-675 [doi]
• A unifying framework for detecting outliers and change points from non-stationary time series dataKenji Yamanishi, Jun-ichi Takeuchi. 676-681 [doi]
• CLOPE: a fast and effective clustering algorithm for transactional dataYiling Yang, Xudong Guan, Jinyuan You. 682-687 [doi]
• Topic-conditioned novelty detectionYiming Yang, Jian Zhang, Jaime G. Carbonell, Chun Jin. 688-693 [doi]
• Transforming classifier scores into accurate multiclass probability estimatesBianca Zadrozny, Charles Elkan. 694-699 [doi]