BibSLEIGH — Proceedings of the 10th International Conference on Knowledge Discovery and Data Mining

Won Kim, Ron Kohavi, Johannes Gehrke, William DuMouchel
Proceedings of the 10th International Conference on Knowledge Discovery and Data Mining
KDD, 2004.

KER

@proceedings{KDD-2004,
	address       = "Seattle, Washington, USA",
	editor        = "Won Kim and Ron Kohavi and Johannes Gehrke and William DuMouchel",
	isbn          = "1-58113-888-1",
	publisher     = "{ACM}",
	title         = "{Proceedings of the 10th International Conference on Knowledge Discovery and Data Mining}",
	year          = 2004,
}

Contents (112 items)

KDD-2004-Haseltine #design: User-centered design for KDD (EH), p. 1.
KDD-2004-Heckerman #data mining #mining #modelling #visual notation: Graphical models for data mining (DH), p. 2.
KDD-2004-AbeZL #learning #multi: An iterative method for multi-class cost-sensitive learning (NA, BZ, JL), pp. 3–11.
KDD-2004-AfratiGM #approximate #set: Approximating a collection of frequent sets (FNA, AG, HM), pp. 12–19.
KDD-2004-AgichteinG #automation #mining #segmentation: Mining reference tables for automatic text segmentation (EA, VG), pp. 20–29.
KDD-2004-AiroldiF #network: Recovering latent time-series from their observed sums: network tomography with particle filters (EA, CF), pp. 30–39.
KDD-2004-AndersonMCN #performance: Fast nonlinear regression via eigenimages applied to galactic morphology (BA, AWM, AJC, RN), pp. 40–48.
KDD-2004-BagnallJ #clustering #modelling: Clustering time series from ARMA models with clipped data (AJB, GJJ), pp. 49–58.
KDD-2004-BasuBM #clustering #framework #probability: A probabilistic framework for semi-supervised clustering (SB, MB, RJM), pp. 59–68.
KDD-2004-CaruanaN #analysis #data mining #empirical #learning #metric #mining #performance: Data mining in metric space: an empirical analysis of supervised learning performance criteria (RC, ANM), pp. 69–78.
KDD-2004-ChakrabartiPMF #automation: Fully automatic cross-associations (DC, SP, DSM, CF), pp. 79–88.
KDD-2004-CohenS #integration #markov #process: Exploiting dictionaries in named entity extraction: combining semi-Markov extraction processes and data integration methods (WWC, SS), pp. 89–98.
KDD-2004-DalviDMSV #classification: Adversarial classification (NND, PMD, M, SKS, DV), pp. 99–108.
KDD-2004-EvgeniouP #learning #multi: Regularized multi--task learning (TE, MP), pp. 109–117.
KDD-2004-FaloutsosMT #performance: Fast discovery of connection subgraphs (CF, KSM, AT), pp. 118–127.
KDD-2004-Fan #concept #data type: Systematic data selection to mine concept-drifting data streams (WF), pp. 128–137.
KDD-2004-GadeWK #constraints #mining #performance: Efficient closed pattern mining in the presence of tough block constraints (KG, JW, GK), pp. 138–147.
KDD-2004-HeCH #approach #correlation #interface #mining #query #web: Discovering complex matchings across web query interfaces: a correlation mining approach (BH, KCCC, JH), pp. 148–157.
KDD-2004-HorvathGW #graph #kernel #mining #predict: Cyclic pattern kernels for predictive graph mining (TH, TG, SW), pp. 158–167.
KDD-2004-HuL #mining: Mining and summarizing customer reviews (MH, BL), pp. 168–177.
KDD-2004-JaroszewiczS #network #using: Interestingness of frequent itemsets using Bayesian networks as background knowledge (SJ, DAS), pp. 178–186.
KDD-2004-JehW #graph #mining: Mining the space of graph properties (GJ, JW), pp. 187–196.
KDD-2004-JinZM #analysis #mining #probability #semantics #web: Web usage mining based on probabilistic latent semantic analysis (XJ, YZ, BM), pp. 197–205.
KDD-2004-KeoghLR #data mining #mining #towards: Towards parameter-free data mining (EJK, SL, C(R), pp. 206–215.
KDD-2004-KumarMS #approach #graph: A graph-theoretic approach to extract storylines from search results (RK, UM, DS), pp. 216–225.
KDD-2004-LiCTW #incremental #maintenance: Incremental maintenance of quotient cube for median (CL, GC, AKHT, SW), pp. 226–235.
KDD-2004-MamoulisCKHTC #mining #query: Mining, indexing, and querying historical spatiotemporal data (NM, HC, GK, MH, YT, DWC), pp. 236–245.
KDD-2004-Muslea #machine learning #online #query: Machine learning for online query relaxation (IM), pp. 246–255.
KDD-2004-NeillM #agile #clustering #detection: Rapid detection of significant spatial clusters (DBN, AWM), pp. 256–265.
KDD-2004-RamakrishnanKMPH #algorithm #mining: Turning CARTwheels: an alternating algorithm for mining redescriptions (NR, DK, BM, MP, RFH), pp. 266–275.
KDD-2004-ShavlikS #detection #effectiveness #evaluation: Selection, combination, and evaluation of effective software sensors for detecting abnormal computer usage (JWS, MS), pp. 276–285.
KDD-2004-SmithE #framework #network: A Bayesian network framework for reject inference (ATS, CE), pp. 286–295.
KDD-2004-SteinbachTK: Support envelopes: a technique for exploring the structure of association patterns (MS, PNT, VK), pp. 296–305.
KDD-2004-SteyversSRG #modelling #probability #topic: Probabilistic author-topic models for information discovery (MS, PS, MRZ, TLG), pp. 306–315.
KDD-2004-WangWPZS #database #graph #mining #scalability: Scalable mining of large disk-based graph databases (CW, WW, JP, YZ, BS), pp. 316–325.
KDD-2004-WuS #information management: Incorporating prior knowledge with weighted margin support vector machines (XW, RKS), pp. 326–333.
KDD-2004-XiongSTK #bound #correlation #identification: Exploiting a support-based upper bound of Pearson’s correlation coefficient for efficiently identifying strongly correlated pairs (HX, SS, PNT, VK), pp. 334–343.
KDD-2004-Yang #complexity #mining: The complexity of mining maximal frequent itemsets and maximal frequent patterns (GY), pp. 344–353.
KDD-2004-YeJL #image #named #performance #reduction #retrieval: GPCA: an efficient dimension reduction scheme for image compression and retrieval (JY, RJ, QL), pp. 354–363.
KDD-2004-YePLJXK #algorithm #composition #incremental #named #reduction: IDR/QR: an incremental dimension reduction algorithm via QR decomposition (JY, QL, HX, HP, RJ, VK), pp. 364–373.
KDD-2004-ZhangPT #on the #statistics: On the discovery of significant statistical quantitative rules (HZ, BP, AT), pp. 374–383.
KDD-2004-ZhangMCS #mining #performance: Fast mining of spatial collocations (XZ, NM, DWC, YS), pp. 384–393.
KDD-2004-AliS #architecture #collaboration #distributed #named #recommendation #using: TiVo: making show recommendations using a distributed collaborative filtering architecture (KA, WvS), pp. 394–401.
KDD-2004-CumbyFGK #predict: Predicting customer shopping lists from point-of-sale purchase data (CMC, AEF, RG, MK), pp. 402–409.
KDD-2004-DengPML #rank: A rank sum test method for informative gene discovery (LD, JP, JM, DLL), pp. 410–419.
KDD-2004-Donoho #detection: Early detection of insider trading in option markets (SD), pp. 420–429.
KDD-2004-JiangPRTZ #array #clustering #mining: Mining coherent gene clusters from gene-sample-time microarray data (DJ, JP, MR, CT, AZ), pp. 430–439.
KDD-2004-IdeK #detection: Eigenspace-based anomaly detection in computer systems (TI, HK), pp. 440–449.
KDD-2004-LazarevicKK #detection #effectiveness #locality #scalability: Effective localized regression for damage detection in large complex mechanical structures (AL, RK, CK), pp. 450–459.
KDD-2004-LinKLLN #mining #monitoring #visual notation: Visually mining and monitoring massive time series (JL, EJK, SL, JPL, DMN), pp. 460–469.
KDD-2004-KolterM #bytecode #detection #learning: Learning to detect malicious executables in the wild (JZK, MAM), pp. 470–478.
KDD-2004-YanVS #predict: Predicting prostate cancer recurrence via maximizing the concordance index (LY, DV, OS), pp. 479–485.
KDD-2004-YoshidaAWMHNFY #detection: Density-based spam detector (KY, FA, TW, HM, TH, AN, HF, KY), pp. 486–493.
KDD-2004-ZhaoLTS #coordination #design #named #parallel #testing #using: V-Miner: using enhanced parallel coordinates to mine product design and test data (KZ, BL, TMT, AS), pp. 494–502.
KDD-2004-AggarwalHWY #classification #data type #on the: On demand classification of data streams (CCA, JH, JW, PSY), pp. 503–508.
KDD-2004-BanerjeeDGMM #approach #approximate #clustering #matrix: A generalized maximum entropy approach to bregman co-clustering and matrix approximation (AB, ISD, JG, SM, DSM), pp. 509–514.
KDD-2004-BanerjeeL #clustering #evaluation: An objective evaluation criterion for clustering (AB, JL), pp. 515–520.
KDD-2004-BiZB #kernel: Column-generation boosting methods for mixture of kernels (JB, TZ, KPB), pp. 521–526.
KDD-2004-ChengYH #database #incremental #mining #named #scalability: IncSpan: incremental mining of sequential patterns in large database (HC, XY, JH), pp. 527–532.
KDD-2004-ChilsonNWZ #correlation #matrix #parallel #robust: Parallel computation of high dimensional robust correlation and covariance matrices (JC, RTN, AW, RHZ), pp. 533–538.
KDD-2004-DasMS: Belief state approaches to signaling alarms in surveillance systems (KD, AWM, JGS), pp. 539–544.
KDD-2004-DavidsonP #image: Locating secret messages in images (ID, GP), pp. 545–550.
KDD-2004-DhillonGK #clustering #kernel #normalisation: Kernel k-means: spectral clustering and normalized cuts (ISD, YG, BK), pp. 551–556.
KDD-2004-EsterGJH #data mining #mining #problem #segmentation: A microeconomic data mining problem: customer-oriented catalog segmentation (ME, RG, WJ, ZH), pp. 557–562.
KDD-2004-GilburdSW #distributed #named #privacy #scalability: k-TTP: a new privacy model for large-scale distributed environments (BG, AS, RW), pp. 563–568.
KDD-2004-Hooker #estimation: Diagnosing extrapolation: tree-based density estimation (GH), pp. 569–574.
KDD-2004-Hooker04a #black box: Discovering additive structure in black box functions (GH), pp. 575–580.
KDD-2004-HuanWPY #database #graph #mining #named: SPIN: mining maximal frequent subgraphs from graph databases (JH, WW, JP, JY), pp. 581–586.
KDD-2004-Iyengar #clustering #detection #on the: On detecting space-time clusters (VSI), pp. 587–592.
KDD-2004-JensenNG #classification #relational #why: Why collective inference improves relational classification (DJ, JN, BG), pp. 593–598.
KDD-2004-KantarciogluJC #data mining #mining #privacy #question: When do data mining results violate privacy? (MK, JJ, CC), pp. 599–604.
KDD-2004-KolczCA #detection #robust: Improved robustness of signature-based near-replica detection via lexicon randomization (AK, AC, JA), pp. 605–610.
KDD-2004-KummamuruKA #difference #learning #metric: Learning spatially variant dissimilarity (SVaD) measures (KK, RK, RA), pp. 611–616.
KDD-2004-LiHY #clustering: Clustering moving objects (YL, JH, JY), pp. 617–622.
KDD-2004-LiuWY #clustering #framework: A framework for ontology-driven subspace clustering (JL, WW, JY), pp. 623–628.
KDD-2004-LiuYM #algorithm #classification #parametricity #performance: The IOC algorithm: efficient many-class non-parametric classification for high-dimensional data (TL, KY, AWM), pp. 629–634.
KDD-2004-MelkmanS #clustering: Sleeved coclustering (AAM, ES), pp. 635–640.
KDD-2004-NatsevNS #mining #multi #representation #semantics: Semantic representation: search and mining of multimedia content (AN, MRN, JRS), pp. 641–646.
KDD-2004-NijssenK #difference #mining: A quickstart in frequent structure mining can make a difference (SN, JNK), pp. 647–652.
KDD-2004-PanYFD #automation #correlation #multi: Automatic multimedia cross-modal correlation discovery (JYP, HJY, CF, PD), pp. 653–658.
KDD-2004-Poole #approach: Estimating the size of the telephone universe: a Bayesian Mark-recapture approach (DP), pp. 659–664.
KDD-2004-PopesculU #clustering #concept #learning #relational #statistics: Cluster-based concept invention for statistical relational learning (AP, LHU), pp. 665–670.
KDD-2004-RusmevichientongZS #identification: Identifying early buyers from purchase data (PR, SZ, DS), pp. 671–677.
KDD-2004-SanilKLR #distributed #modelling #privacy: Privacy preserving regression modelling via distributed computation (APS, AFK, XL, JPR), pp. 677–682.
KDD-2004-SeppanenM: Dense itemsets (JKS, HM), pp. 683–688.
KDD-2004-SteinbachTXK: Generalizing the notion of support (MS, PNT, HX, VK), pp. 689–694.
KDD-2004-TanJ #multi: Ordering patterns by combining opinions from multiple sources (PNT, RJ), pp. 695–700.
KDD-2004-TinoKS #approach #generative #probability #sequence #set #visualisation: A generative probabilistic approach to visualizing sets of symbolic sequences (PT, AK, YS), pp. 701–706.
KDD-2004-VlachosGD #distance #invariant #metric: Rotation invariant distance measures for trajectories (MV, DG, GD), pp. 707–712.
KDD-2004-WrightY #distributed #network #privacy #semistructured data: Privacy-preserving Bayesian network structure computation on distributed heterogeneous data (RNW, ZY), pp. 713–718.
KDD-2004-WuGH #clustering #mining #network #using: Mining scale-free networks using geodesic clustering (AYW, MG, JH), pp. 719–724.
KDD-2004-YanZYYLCXFMC #incremental #named: IMMC: incremental maximum margin criterion (JY, BZ, SY, QY, HL, ZC, WX, WF, WYM, QC), pp. 725–730.
KDD-2004-YangLHG #mining #named #performance #query #xml: 2PXMiner: an efficient two pass mining of frequent XML query patterns (LHY, MLL, WH, XG), pp. 731–736.
KDD-2004-YuL #array #feature model: Redundancy based feature selection for microarray data (LY, HL), pp. 737–742.
KDD-2004-ZhaiVY #comparative #mining: A cross-collection mixture model for comparative text mining (CZ, AV, BY), pp. 743–748.
KDD-2004-ZhangZK #approach #category theory #data mining #image #mining #modelling: A data mining approach to modeling relationships among categories in image collection (RZ, Z(Z, SK), pp. 749–754.
KDD-2004-ZhengPZ #approach: A DEA approach for model combination (Z(Z, BP, HZ), pp. 755–760.
KDD-2004-ZhuL #data mining #mining #privacy: Optimal randomization for privacy preserving data mining (MYZ, LL), pp. 761–766.
KDD-2004-AbeVAS #learning: Cross channel optimized marketing by reinforcement learning (NA, NKV, CA, RS), pp. 767–772.
KDD-2004-AksoyKTM #classification #image #interactive #mining: Interactive training of advanced classifiers for mining remote sensing image archives (SA, KK, CT, GBM), pp. 773–782.
KDD-2004-BorgsCMS #community: Exploring the community structure of newsgroups (CB, JTC, MM, AS), pp. 783–787.
KDD-2004-Cantu-PazNK #feature model: Feature selection in scientific applications (ECP, SDN, CK), pp. 788–793.
KDD-2004-DavidsonGST #algorithm #approach #data mining #matrix #mining #quality: A general approach to incorporate data quality matrices into data mining algorithms (ID, AG, AS, GKT), pp. 794–798.
KDD-2004-AbajoDLC #case study #data mining #delivery #industrial #mining #modelling #quality: ANN quality diagnostic models for packaging manufacturing: an industrial data mining case study (NdA, ABD, VL, SRC), pp. 799–804.
KDD-2004-KalagnanamSVPW #automation: A system for automated mapping of bill-of-materials part numbers (JK, MS, SV, MP, YWW), pp. 805–810.
KDD-2004-MorinagaY #finite #roadmap #topic #using: Tracking dynamics of topic trends using a finite mixture model (SM, KY), pp. 811–816.
KDD-2004-NakataT #mining #predict: Mining traffic data from probe-car system for travel time prediction (TN, JiT), pp. 817–822.
KDD-2004-Ordonez #algorithm #clustering #programming #sql: Programming the K-means clustering algorithm in SQL (CO), pp. 823–828.
KDD-2004-PavlovBDKP #classification #clustering #documentation #multi #naive bayes #preprocessor: Document preprocessing for naive Bayes classification and clustering with mixture of multinomials (DP, RB, BD, SK, JP), pp. 829–834.
KDD-2004-TruongLB #dataset #learning #random #using: Learning a complex metabolomic dataset using random forests and support vector machines (YT, XL, CB), pp. 835–840.
KDD-2004-VogelW #modelling: 1-dimensional splines as building blocks for improving accuracy of risk outcomes models (DSV, MCW), pp. 841–846.
KDD-2004-YehTJS: Analytical view of business data (AY, JT, YJ, SS), pp. 847–852.

Won Kim, Ron Kohavi, Johannes Gehrke, William DuMouchelProceedings of the 10th International Conference on Knowledge Discovery and Data MiningKDD, 2004.

Contents (112 items)

Won Kim, Ron Kohavi, Johannes Gehrke, William DuMouchel
Proceedings of the 10th International Conference on Knowledge Discovery and Data Mining
KDD, 2004.