8. KDD 2002: Edmonton, Alberta, Canada

@proceedings{DBLP:conf/kdd/2002,
  title     = {Proceedings of the Eighth ACM SIGKDD International Conference
               on Knowledge Discovery and Data Mining, July 23-26, 2002, Edmonton,
               Alberta, Canada},
  booktitle = {KDD},
  publisher = {ACM},
  year      = {2002},
  isbn      = {1-58113-567-X},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

Statistical methods I

• Greg Ridgeway, David Madigan:
  Bayesian analysis of massive datasets via particle filters. 5-13
  
• Fatemah A. Alqallaf, Kjell P. Konis, R. Douglas Martin, Ruben H. Zamar:
  Scalable robust covariance and correlation estimates for data mining. 14-23
  
• Kristin P. Bennett, Michinari Momma, Mark J. Embrechts:
  MARK: a boosting algorithm for heterogeneous kernel models. 24-31
  

Frequent patterns I

• Pang-Ning Tan, Vipin Kumar, Jaideep Srivastava:
  Selecting the right interestingness measure for association patterns. 32-41
  
• Cristian Bucila, Johannes Gehrke, Daniel Kifer, Walker M. White:
  DualMiner: a dual-pruning algorithm for itemsets with constraints. 42-51
  
• Alexander Tuzhilin, Bing Liu:
  Querying multiple sets of discovered rules. 52-60
  

Graphs and trees

• Matthew Richardson, Pedro Domingos:
  Mining knowledge-sharing sites for viral marketing. 61-70
  
• Mohammed Javeed Zaki:
  Efficiently mining frequent trees in a forest. 71-80
  
• Christopher R. Palmer, Phillip B. Gibbons, Christos Faloutsos:
  ANF: a fast and scalable tool for data mining in massive graphs. 81-90
  

Streams and time series

• Jon M. Kleinberg:
  Bursty and hierarchical structure in streams. 91-101
  
• Eamonn J. Keogh, Shruti Kasetty:
  On the need for time series data mining benchmarks: a survey and empirical demonstration. 102-111
  

Visualization

• Chris Stolte, Diane Tang, Pat Hanrahan:
  Query, analysis, and visualization of hierarchically structured data using Polaris. 112-122
  
• Jörg A. Walter, Helge Ritter:
  On interactive visualization of high-dimensional data using the hyperbolic plane. 123-132
  

Web search and navigation

• Thorsten Joachims:
  Optimizing search engines using clickthrough data. 133-142
  
• Corin R. Anderson, Pedro Domingos, Daniel S. Weld:
  Relational Markov models and their application to adaptive web navigation. 143-152
  

Sequences and strings

• Darya Chudova, Padhraic Smyth:
  Pattern discovery in sequences under a Markov assumption. 153-162
  
• Charu C. Aggarwal:
  On effective classification of strings with wavelets. 163-172
  

Statistical methods II

• Deepak K. Agarwal:
  Shrinkage estimator generalizations of Proximal Support Vector Machines. 173-182
  
• Jeremy Tantrum, Alejandro Murua, Werner Stuetzle:
  Hierarchical model-based clustering of large datasets through fractionation and refractionation. 183-190
  

Text classification

• Inderjit S. Dhillon, Subramanyam Mallela, Rahul Kumar:
  Enhanced word clustering for hierarchical text classification. 191-200
  
• Canasai Kruengkrai, Chuleerat Jaruskulchai:
  A parallel learning algorithm for text classification. 201-206
  
• Haoran Wu, Tong-Heng Phang, Bing Liu, Xiaoli Li:
  A refinement approach to handling model misfit in text categorization. 207-216
  

Frequent patterns II

• Alexandre V. Evfimievski, Ramakrishnan Srikant, Rakesh Agrawal, Johannes Gehrke:
  Privacy preserving mining of association rules. 217-228
  
• Junqiang Liu, Yunhe Pan, Ke Wang, Jiawei Han:
  Mining frequent item sets by opportunistic projection. 229-238
  

Web page classification

• Hwanjo Yu, Jiawei Han, Kevin Chen-Chuan Chang:
  PEBL: positive example based learning for Web page classification using SVM. 239-248
  
• Martin Ester, Hans-Peter Kriegel, Matthias Schubert:
  Web site mining: a new way to spot competitors, customers and suppliers in the world wide web. 249-258
  

Learning methods

• Edwin P. D. Pednault, Naoki Abe, Bianca Zadrozny:
  Sequential cost-sensitive decision making with reinforcement learning. 259-268
  
• Sunita Sarawagi, Anuradha Bhamidipaty:
  Interactive deduplication using active learning. 269-278
  

Intrusion and privacy

• Vijay S. Iyengar:
  Transforming data to satisfy privacy constraints. 279-288
  
• Kristin P. Bennett, Ayhan Demiriz, Richard Maclin:
  Exploiting unlabeled data in ensemble methods. 289-296
  

Ensembles and boosting

• Mahesh V. Joshi, Ramesh C. Agarwal, Vipin Kumar:
  Predicting rare classes: can boosting make any weak learner strong? 297-306
  
• Aleksander Kolcz, Xiaomei Sun, Jugal K. Kalita:
  Efficient handling of high-dimensional feature spaces by randomized classifier ensembles. 307-313
  

Industry track papers

• Mohammad El-Ramly, Eleni Stroulia, Paul G. Sorenson:
  From run-time behavior to usage scenarios: an interaction-pattern mining approach. 315-324
  
• Andrew Storey, Marc-David Cohen:
  Exploiting response models: optimizing cross-sell and up-sell opportunities in banking. 325-331
  
• Saharon Rosset, Einat Neumann, Uri Eick, Nurit Vatnik, Yizhak Idan:
  Customer lifetime value modeling and its use for customer retention planning. 332-340
  
• Satoshi Morinaga, Kenji Yamanishi, Kenji Tateishi, Toshikazu Fukushima:
  Mining product reputations on the Web. 341-349
  
• Sheila Tejada, Craig A. Knoblock, Steven Minton:
  Learning domain-independent string transformation weights for high accuracy object identification. 350-359
  
• Sholom M. Weiss, Naval K. Verma:
  A system for real-time competitive market intelligence. 360-365
  
• Klaus Julisch, Marc Dacier:
  Mining intrusion detection alarms for actionable knowledge. 366-375
  
• Matthew V. Mahoney, Philip K. Chan:
  Learning nonstationary models of normal network traffic for detecting novel attacks. 376-385
  
• Karlton Sequeira, Mohammed Javeed Zaki:
  ADMIT: anomaly-based data mining for intrusions. 386-395
  
• Alexander Tuzhilin, Gediminas Adomavicius:
  Handling very large numbers of association rules in the analysis of microarray data. 396-404
  
• Peter Antal, Patrick Glenisson, Geert Fannes:
  On the potential of domain literature for clustering and Bayesian network learning. 405-414
  
• Yulan Liang, Arpad Kelemen:
  Mining heterogeneous gene expression data with time lagged recurrent neural networks. 415-421
  

Poster papers

• Charu C. Aggarwal:
  Collaborative crawling: mining user experiences for topical resource discovery. 423-428
  
• Jay Ayres, Jason Flannick, Johannes Gehrke, Tomi Yiu:
  Sequential PAttern mining using a bitmap representation. 429-435
  
• Florian Beil, Martin Ester, Xiaowei Xu:
  Frequent term-based text clustering. 436-442
  
• Shai Ben-David, Johannes Gehrke, Reba Schuller:
  A theoretical framework for learning from a pool of disparate data sources. 443-449
  
• Ella Bingham, Heikki Mannila, Jouni K. Seppänen:
  Topics in 0--1 data. 450-455
  
• Olcay Boz:
  Extracting decision trees from trained neural networks. 456-461
  
• Bin Chen, Peter J. Haas, Peter Scheuermann:
  A new two-phase sampling based algorithm for discovering association rules. 462-468
  
• Christina Yip Chung, Bin Chen:
  CVS: a Correlation-Verification based Smoothing technique on information retrieval and term clustering. 469-474
  
• William W. Cohen, Jacob Richman:
  Learning to match and cluster large high-dimensional data sets for data integration. 475-480
  
• Alin Dobra, Johannes Gehrke:
  SECRET: a scalable linear regression tree algorithm. 481-487
  
• Tina Eliassi-Rad, Terence Critchlow, Ghaleb Abdulla:
  Tina Eliassi-Rad, Terence Critchlow, Ghaleb Abdulla. 488-494
  
• Bin Fang, Wynne Hsu, Mong-Li Lee:
  Tumor cell identification using features rules. 495-500
  
• Dimitris Fragoudis, Dimitris Meretakis, Spiros Likothanassis:
  Integrating feature and instance selection for text classification. 501-506
  
• Hichem Frigui:
  SyMP: an efficient clustering approach to identify clusters of arbitrary shapes in large data sets. 507-512
  
• Shantanu Godbole, Sunita Sarawagi, Soumen Chakrabarti:
  Scaling multi-class support vector machines using inter-class confusion. 513-518
  
• Tu Bao Ho, Trong Dung Nguyen, DucDung Nguyen:
  Visualization support for a user-centered KDD process. 519-524
  
• Geoff Hulten, Pedro Domingos:
  Mining complex models from arbitrarily large databases in constant time. 525-531
  
• Srinivasan Jagannathan, Jayanth Nayak, Kevin C. Almeroth, Markus Hofmann:
  A model for discovering customer value for E-content. 532-537
  
• Glen Jeh, Jennifer Widom:
  SimRank: a measure of structural-context similarity. 538-543
  
• Xiaoming Jin, Yuchang Lu, Chunyi Shi:
  Similarity measure based on partial information of time series. 544-549
  
• Eamonn J. Keogh, Stefano Lonardi, Bill Yuan-chi Chiu:
  Finding surprising patterns in a time series database in linear time and space. 550-556
  
• Mahesh Kumar, Nitin R. Patel, Jonathan Woo:
  Clustering seasonality patterns in the presence of errors. 557-563
  
• Jiuyong Li, Rodney W. Topor, Hong Shen:
  Construct robust rule sets for classification. 564-569
  
• Ruey-Hsia Li, Geneva G. Belford:
  Instability of decision tree classification algorithms. 570-575
  
• Cheng-Ru Lin, Chang-Hung Lee, Ming-Syan Chen, Philip S. Yu:
  Distributed data mining in a chain store database of short transactions. 576-581
  
• Cheng-Ru Lin, Ming-Syan Chen:
  A robust and efficient clustering algorithm based on cohesion self-merging. 582-587
  
• Shian-Hua Lin, Jan-Ming Ho:
  Discovering informative content blocks from Web documents. 588-593
  
• Bertis B. Little, Walter L. Johnston, Ashley C. Lovell, Roderick M. Rejesus, Steve A. Steed:
  Collusion in the U.S. crop insurance program: applied data mining. 594-598
  
• Rey-Long Liu, Yun-Ling Lu:
  Incremental context mining for adaptive document classification. 599-604
  
• Anna Olecka:
  Evaluating classifiers' performance in a constrained environment. 605-612
  
• Patrick Pantel, Dekang Lin:
  Discovering word senses from text. 613-619
  
• Bhavani Raskutti, Herman L. Ferrá, Adam Kowalczyk:
  Combining clustering and co-training to enhance text classification using unlabelled data. 620-625
  
• Naonori Ueda, Kazumi Saito:
  Single-shot detection of multiple categories of text using parametric mixture models. 626-631
  
• Secil Ugurel, Robert Krovetz, C. Lee Giles:
  What's the code?: automatic classification of source code archives. 639-644
  
• Jaideep Vaidya, Chris Clifton:
  Privacy preserving association rule mining in vertically partitioned data. 639-644
  
• Michail Vlachos, Carlotta Domeniconi, Dimitrios Gunopulos, George Kollios, Nick Koudas:
  Non-linear dimensionality reduction techniques for classification and visualization. 645-651
  
• Ke Wang, Ming-Yen Thomas Su:
  Item selection by "hub-authority" profit ranking. 652-657
  
• Vasa Curcin, Moustafa Ghanem, Yike Guo, Martin Köhler, Anthony Rowe, Jameel Syed, Patrick Wendel:
  Discovery net: towards a grid of knowledge discovery. 658-663
  
• Leejay Wu, Christos Faloutsos:
  Making every bit count: fast nonlinear axis scaling. 664-669
  
• Xintao Wu, Jianping Fan, Kalpathi R. Subramanian:
  B-EM: a classifier incorporating bootstrap with EM approach for data mining. 670-675
  
• Kenji Yamanishi, Jun-ichi Takeuchi:
  A unifying framework for detecting outliers and change points from non-stationary time series data. 676-681
  
• Yiling Yang, Xudong Guan, Jinyuan You:
  CLOPE: a fast and effective clustering algorithm for transactional data. 682-687
  
• Yiming Yang, Jian Zhang, Jaime G. Carbonell, Chun Jin:
  Topic-conditioned novelty detection. 688-693
  
• Bianca Zadrozny, Charles Elkan:
  Transforming classifier scores into accurate multiclass probability estimates. 694-699