BibSLEIGH — Proceedings of the 16th International Conference on Knowledge Discovery and Data Mining

Bharat Rao, Balaji Krishnapuram, Andrew Tomkins, Qiang Yang
Proceedings of the 16th International Conference on Knowledge Discovery and Data Mining
KDD, 2010.

KER

@proceedings{KDD-2010,
	address       = "Washington, District of Columbia, USA",
	editor        = "Bharat Rao and Balaji Krishnapuram and Andrew Tomkins and Qiang Yang",
	isbn          = "978-1-4503-0055-1",
	publisher     = "{ACM}",
	title         = "{Proceedings of the 16th International Conference on Knowledge Discovery and Data Mining}",
	year          = 2010,
}

Contents (125 items)

KDD-2010-Lu #data mining #industrial #mining #online: Data mining in the online services industry (QL), pp. 1–2.
KDD-2010-Freund: Data winnowing (YF), pp. 3–4.
KDD-2010-Feldman #data mining #lessons learnt #mining #quantifier #scalability: The quantification of advertising: (+ lessons from building businesses based on large scale data mining) (KF), pp. 5–6.
KDD-2010-ChanGGHL #modelling #online #pipes and filters #scalability: Evaluating online ad campaigns in a pipeline: causal models at scale (DC, RG, OG, TH, DL), pp. 7–16.
KDD-2010-TangAOM #empirical #framework #performance: Overlapping experiment infrastructure: more, better, faster experimentation (DT, AA, DO, MM), pp. 17–26.
KDD-2010-LiWZCMJ #performance: Exploitation and exploration in a performance based contextual advertising system (WL, XW, RZ, YC, JM, RJ), pp. 27–36.
KDD-2010-KarguptaSG #data mining #distributed #mining #overview #performance: MineFleet®: an overview of a widely adopted distributed vehicle performance data mining system (HK, KS, MG), pp. 37–46.
KDD-2010-DasMSO #algorithm #case study #detection #kernel #learning #multi #safety: Multiple kernel learning for heterogeneous anomaly detection: algorithm and aviation safety case study (SD, BLM, ANS, NCO), pp. 47–56.
KDD-2010-GoorhaU #roadmap: Discovery of significant emerging trends (SG, LHU), pp. 57–64.
KDD-2010-KumarGM #data mining #fault #health #mining #predict: Data mining to predict and prevent errors in health insurance claims processing (MK, RG, ZSM), pp. 65–74.
KDD-2010-AbeMPRJTBACKDG #learning #optimisation #using: Optimizing debt collections using constrained reinforcement learning (NA, PM, CP, CKR, DLJ, VPT, JJB, GFA, BRC, MK, MD, TG), pp. 75–84.
KDD-2010-CaoOYW #behaviour #detection #sequence: Detecting abnormal coupled sequences and sequence changes in group-based manipulative trading behaviors (LC, YO, PSY, GW), pp. 85–94.
KDD-2010-YeLCJ #automation #categorisation #clustering #using: Automatic malware categorization using cluster ensemble (YY, TL, YC, QJ), pp. 95–104.
KDD-2010-BozorgiSSV #heuristic #learning #predict: Beyond heuristics: learning to classify vulnerabilities and predict exploits (MB, LKS, SS, GMV), pp. 105–114.
KDD-2010-MaxwellBR #graph #memory management #mining #using: Diagnosing memory leaks using graph mining on heap dumps (EKM, GB, NR), pp. 115–124.
KDD-2010-ZhengSTLLCH #data mining #mining #network #using: Using data mining techniques to address critical information exchange needs in disaster affected public-private networks (LZ, CS, LT, TL, SL, SCC, VH), pp. 125–134.
KDD-2010-HoTL #learning #metric #reduction #sequence #similarity: Tropical cyclone event sequence similarity search via dimensionality reduction and metric learning (SSH, WT, WTL), pp. 135–144.
KDD-2010-BennetGLSV #benchmark #metric #named #scalability #towards: Malstone: towards a benchmark for analytics on large data clouds (CB, RLG, DL, JS, SV), pp. 145–152.
KDD-2010-WeiLSPZQSTZ #named #visual notation: TIARA: a visual exploratory text analytic system (FW, SL, YS, SP, MXZ, WQ, LS, LT, QZ), pp. 153–162.
KDD-2010-HendersonEFALMPT #approach #forensics #graph #metric #mining #multi: Metric forensics: a multi-level approach for mining volatile graphs (KH, TER, CF, LA, LL, KM, BAP, HT), pp. 163–172.
KDD-2010-WallaceSBT #learning: Active learning for biomedical citation screening (BCW, KS, CEB, TAT), pp. 173–182.
KDD-2010-KhoslaCLCHL #approach #machine learning #predict: An integrated machine learning approach to stroke prediction (AK, YC, CCYL, HKC, JH, HL), pp. 183–192.
KDD-2010-YanFDR #classification: Medical coding classification by leveraging inter-code relationships (YY, GF, JGD, RR), pp. 193–202.
KDD-2010-WangHJTZYG #mining #network #research: Mining advisor-advisee relationships from research publication networks (CW, JH, YJ, JT, DZ, YY, JG), pp. 203–212.
KDD-2010-AgarwalAKK #modelling #multi #scalability: Estimating rates of rare events with multiple hierarchies through scalable log-linear models (DA, RA, RK, NK), pp. 213–222.
KDD-2010-SrikantBWP #modelling: User browsing models: relevance versus examination (RS, SB, NW, DP), pp. 223–232.
KDD-2010-RothBDFHLLMM #graph #social #using: Suggesting friends using the implicit social graph (MR, ABD, DD, GF, IH, AL, NL, YM, RM), pp. 233–242.
KDD-2010-LichtenwalterLC #predict: New perspectives and methods in link prediction (RL, JTL, NVC), pp. 243–252.
KDD-2010-TsengWSY #algorithm #mining #named #performance: UP-Growth: an efficient algorithm for high utility itemset mining (VST, CWW, BES, PSY), pp. 253–262.
KDD-2010-Ruggieri #mining: Frequent regular itemset mining (SR), pp. 263–272.
KDD-2010-SunCCC #mining #nondeterminism #probability: Mining uncertain data with probabilistic guarantees (LS, RC, DWC, JC), pp. 273–282.
KDD-2010-LamC #data type #flexibility #mining: Mining top-k frequent items in a data stream with flexible sliding windows (HTL, TC), pp. 283–292.
KDD-2010-Tatti: Probably the best itemsets (NT), pp. 293–302.
KDD-2010-ZhuLX #feature model #incremental #learning #markov #named #performance #random: Grafting-light: fast, incremental feature selection and structure learning of Markov random fields (JZ, NL, EPX), pp. 303–312.
KDD-2010-SunCY #approach #reduction #scalability: A scalable two-stage approach for a class of dimensionality reduction techniques (LS, BC, JY), pp. 313–322.
KDD-2010-LiuYY #algorithm #performance #problem: An efficient algorithm for a class of fused lasso problems (JL, LY, JY), pp. 323–332.
KDD-2010-CaiZH #clustering #feature model #multi: Unsupervised feature selection for multi-cluster data (DC, CZ, XH), pp. 333–342.
KDD-2010-YangO #feature model #predict #probability #using: Feature selection for support vector regression using probabilistic prediction (JBY, CJO), pp. 343–352.
KDD-2010-JinZZD #privacy: Versatile publishing for privacy preservation (XJ, MZ, NZ, GD), pp. 353–362.
KDD-2010-LinC #outsourcing #privacy #random: Privacy-preserving outsourcing support vector machines with random transformation (KPL, MSC), pp. 363–372.
KDD-2010-WenL #on the #quality #social: On the quality of inferring interests from social neighbors (ZW, CYL), pp. 373–382.
KDD-2010-SarangiM #named #nondeterminism #similarity: DUST: a generalized notion of similarity between uncertain time series (SRS, KM), pp. 383–392.
KDD-2010-LeroyCB #predict: Cold start link prediction (VL, BBC, FB), pp. 393–402.
KDD-2010-LiuZ #learning: Learning with cost intervals (XYL, ZHZ), pp. 403–412.
KDD-2010-AdaB #composition #generative: The new iris data: modular data generators (IA, MRB), pp. 413–422.
KDD-2010-AttenbergP #classification #learning #modelling #why: Why label when you can search?: alternatives to active learning for applying human resources to build classification models under extreme class imbalance (JA, FJP), pp. 423–432.
KDD-2010-FangNF: Discovering significant relaxed order-preserving submatrices (QF, WN, JF), pp. 433–442.
KDD-2010-HeP #topic: Topic dynamics: an alternative model of bursts in streams of topics (DH, DSPJ), pp. 443–452.
KDD-2010-SundaravaradanHSSVHR #biology #modelling: Extracting temporal signatures for comprehending systems biology models (NS, KSMTH, VS, DJS, JPCV, LSH, NR), pp. 453–462.
KDD-2010-LiLZ #algorithm #collaboration #concept #correlation: Negative correlations in collaboration: concepts and algorithms (JL, QL, TZ), pp. 463–472.
KDD-2010-TaiYC #mining #outsourcing #pseudo #taxonomy: k-Support anonymity based on pseudo taxonomy for outsourcing of frequent itemset mining (CHT, PSY, MSC), pp. 473–482.
KDD-2010-YangNSS #data mining #mining #privacy: Collusion-resistant privacy-preserving data mining (BY, HN, IS, JS), pp. 483–492.
KDD-2010-FriedmanS #data mining #difference #mining #privacy: Data mining with differential privacy (AF, AS), pp. 493–502.
KDD-2010-BhaskarLST: Discovering frequent patterns in sensitive data (RB, SL, AS, AT), pp. 503–512.
KDD-2010-SarkarM #graph #nearest neighbour #performance: Fast nearest-neighbor search in disk-resident graphs (PS, AWM), pp. 513–522.
KDD-2010-AlaeiKMV #representation: Balanced allocation with succinct representation (SA, RK, AM, EV), pp. 523–532.
KDD-2010-MaserratP #network #query #social: Neighbor query friendly compression of social networks (HM, JP), pp. 533–542.
KDD-2010-HeFLC #graph #parallel #scalability: Parallel SimRank computation on large graphs with iterative aggregation (GH, HF, CL, HC), pp. 543–552.
KDD-2010-KumarMM: Dynamics of conversations (RK, MM, MM), pp. 553–562.
KDD-2010-WangD #clustering #flexibility: Flexible constrained spectral clustering (XW, ID), pp. 563–572.
KDD-2010-DangB #clustering #linear: A hierarchical information theoretic technique for the discovery of non linear alternative clusterings (XHD, JB), pp. 573–582.
KDD-2010-BohmPSY #clustering: Clustering by synchronization (CB, CP, JS, QY), pp. 583–592.
KDD-2010-HossainTWDHR #clustering: Unifying dependent clustering and disparate clustering for non-homogeneous data (MSH, ST, LTW, ID, RFH, NR), pp. 593–602.
KDD-2010-MarchRG #algorithm #analysis #performance: Fast euclidean minimum spanning tree: algorithm, analysis, and applications (WBM, PR, AGG), pp. 603–612.
KDD-2010-LouFYLW #mining #workflow: Mining program workflow from interleaved traces (JGL, QF, SY, JL, BW), pp. 613–622.
KDD-2010-ShahafG: Connecting the dots between news articles (DS, CG), pp. 623–632.
KDD-2010-ZouGL #database #graph #nondeterminism #probability #semantics: Discovering frequent subgraphs over uncertain graph databases under probabilistic semantics (ZZ, HG, JL), pp. 633–642.
KDD-2010-FeiH #classification #functional #graph: Boosting with structure information in the functional space: an application to graph classification (HF, JH), pp. 643–652.
KDD-2010-HuhF #learning #modelling #topic: Discriminative topic modeling based on manifold learning (SH, SEF), pp. 653–662.
KDD-2010-IwataYSU #modelling #multi #online #topic: Online multiscale dynamic topic models (TI, TY, YS, NU), pp. 663–672.
KDD-2010-SatoN #modelling #process #topic #using: Topic models with power-law using Pitman-Yor process (IS, HN), pp. 673–682.
KDD-2010-LuHCPHL #social #topic: The topic-perspective model for social tagging systems (CL, XH, XC, JrP, TH, ZL), pp. 683–692.
KDD-2010-JahrerTL #predict #recommendation: Combining predictions for accurate recommender systems (MJ, AT, RAL), pp. 693–702.
KDD-2010-AgarwalCE #learning #online #performance #recommendation: Fast online learning through offline initialization for time-sensitive recommendation (DA, BCC, PE), pp. 703–712.
KDD-2010-Steck #random #recommendation #testing: Training and testing of recommender systems on data missing not at random (HS), pp. 713–722.
KDD-2010-XiangYZCZYS #graph #recommendation: Temporal recommendation on graphs via long- and short-term preference fusion (LX, QY, SZ, LC, XZ, QY, JS), pp. 723–732.
KDD-2010-MiaoMYTCA #generative #modelling #network: Generative models for ticket resolution in expert networks (GM, LEM, XY, ST, YC, NA), pp. 733–742.
KDD-2010-Lee #classification #learning: Learning to combine discriminative classifiers: confidence based (CHL), pp. 743–752.
KDD-2010-LiAZ #mining: Mining positive and negative patterns for relevance feature discovery (YL, AA, NZ), pp. 753–762.
KDD-2010-YuHW #clustering #documentation #feature model #process: Document clustering via dirichlet process mixture model with feature selection (GY, RzH, ZW), pp. 763–772.
KDD-2010-ReichartzKP #dependence #kernel #semantics: Semantic relation extraction with kernels over typed dependency trees (FR, HK, GP), pp. 773–782.
KDD-2010-WangLZ #analysis #approach #overview #rating: Latent aspect rating analysis on review text data: a rating regression approach (HW, YL, CZ), pp. 783–792.
KDD-2010-KongY #classification #feature model #graph: Semi-supervised feature selection for graph classification (XK, PSY), pp. 793–802.
KDD-2010-DuBoisS #modelling #relational: Modeling relational events via latent classes (CD, PS), pp. 803–812.
KDD-2010-GaoLFWSH #community #detection #network #on the #performance: On community outliers and their efficient detection in information networks (JG, FL, WF, CW, YS, JH), pp. 813–822.
KDD-2010-PrestonBKSF #clustering #constraints #using: Redefining class definitions using constraint-based clustering: an application to remote sensing of the earth’s surface (DP, CEB, RK, DSM, MAF), pp. 823–832.
KDD-2010-YuHCL #classification #in memory #linear #memory management #scalability: Large linear classification when data cannot fit in memory (HFY, CJH, KWC, CJL), pp. 833–842.
KDD-2010-PrengerLVCH #bound #classification #fault: Class-specific error bounds for ensemble classifiers (RJP, TDL, KRV, BYC, WGH), pp. 843–852.
KDD-2010-RaykarKY #classification #design #performance #trade-off: Designing efficient cascaded classifiers: tradeoff between accuracy and cost (VCR, BK, SY), pp. 853–860.
KDD-2010-GaoW #mining #nondeterminism: Direct mining of discriminative patterns for classifying uncertain data (CG, JW), pp. 861–870.
KDD-2010-LuWZB: Ensemble pruning via individual contribution ordering (ZL, XW, XZ, JB), pp. 871–880.
KDD-2010-LaoC #execution #modelling #performance #query #random #retrieval: Fast query execution for retrieval models based on path-constrained random walks (NL, WWC), pp. 881–888.
KDD-2010-ChuaL #generative #modelling #network #online #rating #trust #using: Trust network inference for online rating data using generative models (FCTC, EPL), pp. 889–898.
KDD-2010-GeXTXGP #energy #mobile #recommendation: An energy-efficient mobile recommender system (YG, HX, AT, KX, MG, MJP), pp. 899–908.
KDD-2010-SomaiyaJR #learning #modelling: Mixture models for learning low-dimensional roles in high-dimensional data (MS, CMJ, SR), pp. 909–918.
KDD-2010-LiuLNFL #clustering #towards: Towards mobility-based clustering (SL, YL, LMN, JF, ML), pp. 919–928.
KDD-2010-LinZMH #community #named #social #statistics: PET: a statistical model for popular events tracking in social communities (CXL, BZ, QM, JH), pp. 929–938.
KDD-2010-SozioG #how #problem: The community-search problem and how to plan a successful cocktail party (MS, AG), pp. 939–948.
KDD-2010-PlangprasopchokLG #folksonomy #metadata: Growing a tree in the forest: constructing folksonomies by integrating structured metadata (AP, KL, LG), pp. 949–958.
KDD-2010-YinXHD #personalisation #predict #probability: A probabilistic model for personalized tag prediction (DY, ZX, LH, BDD), pp. 959–968.
KDD-2010-LiuNYW #automation #named #wiki: BioSnowball: automated population of Wikis (XL, ZN, NY, JRW), pp. 969–978.
KDD-2010-Sculley #ranking: Combined regression and ranking (DS), pp. 979–988.
KDD-2010-TingZLT #estimation: Mass estimation and its applications (KMT, GTZ, FTL, JSCT), pp. 989–998.
KDD-2010-ZhangZ #dependence #learning #multi: Multi-label learning by exploiting label dependency (MLZ, KZ), pp. 999–1008.
KDD-2010-MeiGR #named #network: DivRank: the interplay of prestige and diversity in information networks (QM, JG, DRR), pp. 1009–1018.
KDD-2010-Gomez-RodriguezLK #network: Inferring networks of diffusion and influence (MGR, JL, AK), pp. 1019–1028.
KDD-2010-ChenWW #network #scalability #social: Scalable influence maximization for prevalent viral marketing in large-scale social networks (WC, CW, YW), pp. 1029–1038.
KDD-2010-WangCSX #algorithm #mining #mobile #network #social: Community-based greedy algorithm for mining top-K influential nodes in mobile social networks (YW, GC, GS, KX), pp. 1039–1048.
KDD-2010-TanTSLW #graph #social: Social action tracking via noise tolerant time-varying factor graphs (CT, JT, JS, QL, FW), pp. 1049–1058.
KDD-2010-LappasTGM #network #social: Finding effectors in social networks (TL, ET, DG, HM), pp. 1059–1068.
KDD-2010-ChenLB #approach #detection #named #statistics: GLS-SOD: a generalized local statistical approach for spatial outlier detection (FC, CTL, APB), pp. 1069–1078.
KDD-2010-ZhangSZL #corpus #correlation #multi #process: Evolutionary hierarchical dirichlet processes for multiple correlated time-varying corpora (JZ, YS, CZ, SL), pp. 1079–1088.
KDD-2010-MueenK #maintenance #online: Online discovery and maintenance of time series motifs (AM, EJK), pp. 1089–1098.
KDD-2010-LiDHKN #behaviour #mining: Mining periodic behaviors for moving objects (ZL, BD, JH, RK, PN), pp. 1099–1108.
KDD-2010-WangC #algorithm #linear #modelling #performance: An efficient causal discovery algorithm for linear models (ZW, LC), pp. 1109–1118.
KDD-2010-DurrantK #analysis #classification #linear: Compressed fisher linear discriminant analysis: classification of randomly projected data (RJD, AK), pp. 1119–1128.
KDD-2010-HeLC #kernel #scalability #similarity: Scalable similarity search with optimized kernel hashing (JH, WL, SFC), pp. 1129–1138.
KDD-2010-LiuMTLL #learning #metric #optimisation #using: Semi-supervised sparse metric learning using alternating linearization optimization (WL, SM, DT, JL, PL), pp. 1139–1148.
KDD-2010-AgarwalPV #multi #scalability: Universal multi-dimensional scaling (AA, JMP, SV), pp. 1149–1158.
KDD-2010-YangJJZT #categorisation #classification: Unsupervised transfer classification: application to text categorization (TY, RJ, AKJ, YZ, WT), pp. 1159–1168.
KDD-2010-GuptaPATV #learning #retrieval #social #social media: Nonnegative shared subspace learning and its application to social media retrieval (SKG, DQP, BA, TT, SV), pp. 1169–1178.
KDD-2010-ChenLY #learning #multi #rank: Learning incoherent sparse and low-rank patterns from multiple tasks (JC, JL, JY), pp. 1179–1188.
KDD-2010-ChapelleSVWZT #learning #multi #ranking #web: Multi-task learning for boosting with application to web search ranking (OC, PKS, SV, KQW, YZ, BLT), pp. 1189–1198.
KDD-2010-ZhangY #learning #metric: Transfer metric learning by learning task relationships (YZ, DYY), pp. 1199–1208.
KDD-2010-KarguptaGF #data mining #generative #mining: The next generation of transportation systems, greenhouse emissions, and data mining (HK, JG, WF), pp. 1209–1212.

Bharat Rao, Balaji Krishnapuram, Andrew Tomkins, Qiang YangProceedings of the 16th International Conference on Knowledge Discovery and Data MiningKDD, 2010.

Contents (125 items)

Bharat Rao, Balaji Krishnapuram, Andrew Tomkins, Qiang Yang
Proceedings of the 16th International Conference on Knowledge Discovery and Data Mining
KDD, 2010.