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

Usama M. Fayyad, Surajit Chaudhuri, David Madigan
Proceedings of the Fifth International Conference on Knowledge Discovery and Data Mining
KDD, 1999.

KER

@proceedings{KDD-1999,
	acmid         = "312129",
	address       = "San Diego, California, USA",
	editor        = "Usama M. Fayyad and Surajit Chaudhuri and David Madigan",
	isbn          = "1-58113-143-7",
	publisher     = "{ACM}",
	title         = "{Proceedings of the Fifth International Conference on Knowledge Discovery and Data Mining}",
	year          = 1999,
}

Contents (64 items)

KDD-1999-Agrawal #data mining #mining: Data Mining: Crossing the Chasm (RA), p. 2.
KDD-1999-Hackathorn #web: Farming the Web for Systematic Business Intelligence (RDH), p. 3.
KDD-1999-Pregibon #named #statistics: 2001: A Statistical Odyssey (DP), p. 4.
KDD-1999-DuMouchelVJCP: Squashing Flat Files Flatter (WD, CV, TJ, CC, DP), pp. 6–15.
KDD-1999-LarsenA #clustering #documentation #effectiveness #linear #mining #performance #using: Fast and Effective Text Mining Using Linear-Time Document Clustering (BL, CA), pp. 16–22.
KDD-1999-ProvostJO #performance: Efficient Progressive Sampling (FJP, DJ, TO), pp. 23–32.
KDD-1999-GuralnikS #detection: Event Detection from Time Series Data (VG, JS), pp. 33–42.
KDD-1999-DongL #difference #mining #performance #roadmap: Efficient Mining of Emerging Patterns: Discovering Trends and Differences (GD, JL), pp. 43–52.
KDD-1999-FawcettP #behaviour #monitoring #process: Activity Monitoring: Noticing Interesting Changes in Behavior (TF, FJP), pp. 53–62.
KDD-1999-GaffneyS #clustering #modelling: Trajectory Clustering with Mixtures of Regression Models (SG, PS), pp. 63–72.
KDD-1999-GantiGR #category theory #clustering #named #summary #using: CACTUS — Clustering Categorical Data Using Summaries (VG, JG, RR), pp. 73–83.
KDD-1999-ChengFZ #clustering #mining: Entropy-based Subspace Clustering for Mining Numerical Data (CHC, AWCF, YZ), pp. 84–93.
KDD-1999-ManiDBD #data mining #mining #modelling #statistics: Statistics and Data Mining Techniques for Lifetime Value Modeling (DRM, JD, AB, PD), pp. 94–103.
KDD-1999-RogersLW #mining #modelling: Mining GPS Data to Augment Road Models (SR, PL, CW), pp. 104–113.
KDD-1999-LeeSM #data flow #detection #experience #mining #network: Mining in a Data-Flow Environment: Experience in Network Intrusion Detection (WL, SJS, KWM), pp. 114–124.
KDD-1999-LiuHM: Pruning and Summarizing the Discovered Associations (BL, WH, YM), pp. 125–134.
KDD-1999-BrinRS #mining: Mining Optimized Gain Rules for Numeric Attributes (SB, RR, KS), pp. 135–144.
KDD-1999-BayardoA #mining: Mining the Most Interesting Rules (RJBJ, RA), pp. 145–154.
KDD-1999-Domingos #classification #named: MetaCost: A General Method for Making Classifiers Cost-Sensitive (PMD), pp. 155–164.
KDD-1999-MeretakisW #classification #naive bayes #using: Extending Naïve Bayes Classifiers Using Long Itemsets (DM, BW), pp. 165–174.
KDD-1999-BonchiGMP #classification #detection: A Classification-Based Methodology for Planning Audit Strategies in Fraud Detection (FB, FG, GM, DP), pp. 175–184.
KDD-1999-Piatetsky-ShapiroM #modelling: Estimating Campaign Benefits and Modeling Lift (GPS, BMM), pp. 185–193.
KDD-1999-Potts #network: Generalized Additive Neural Networks (WJEP), pp. 194–200.
KDD-1999-AggarwalWWY #approach #collaboration #graph: Horting Hatches an Egg: A New Graph-Theoretic Approach to Collaborative Filtering (CCA, JLW, KLW, PSY), pp. 201–212.
KDD-1999-WijsenNC #dependence: Discovering Roll-Up Dependencies (JW, RTN, TC), pp. 213–222.
KDD-1999-ShanmugasundaramFB #approximate #query: Compressed Data Cubes for OLAP Aggregate Query Approximation on Continuous Dimensions (JS, UMF, PSB), pp. 223–232.
KDD-1999-BennettFG #approximate #nearest neighbour #query: Density-Based Indexing for Approximate Nearest-Neighbor Queries (KPB, UMF, DG), pp. 233–243.
KDD-1999-NagDD #interactive #using: Using a Knowledge Cache for Interactive Discovery of Association Rules (BN, PD, DJD), pp. 244–253.
KDD-1999-BrijsSVW #case study #using: Using Association Rules for Product Assortment Decisions: A Case Study (TB, GS, KV, GW), pp. 254–260.
KDD-1999-AumannL #statistics: A Statistical Theory for Quantitative Association Rules (YA, YL), pp. 261–270.
KDD-1999-SyedLS #case study #independence: A Study of Support Vectors on Model Independent Example Selection (NAS, HL, KKS), pp. 272–276.
KDD-1999-PellegM #algorithm #geometry #reasoning: Accelerating Exact k-means Algorithms with Geometric Reasoning (DP, AWM), pp. 277–281.
KDD-1999-HuangY #adaptation #query: Adaptive Query Processing for Time-Series Data (YWH, PSY), pp. 282–286.
KDD-1999-AyanTA #algorithm #performance #scalability: An Efficient Algorithm to Update Large Itemsets with Early Pruning (NFA, AUT, MEA), pp. 287–291.
KDD-1999-Cerquides #induction: Applying General Bayesian Techniques to Improve TAN Induction (JC), pp. 292–296.
KDD-1999-TungLHF #mining #transaction: Breaking the Barrier of Transactions: Mining Inter-Transaction Association Rules (AKHT, HL, JH, LF), pp. 297–301.
KDD-1999-BayP #category theory #data mining #detection #mining #set: Detecting Change in Categorical Data: Mining Contrast Sets (SDB, MJP), pp. 302–306.
KDD-1999-WangWLSSZ #algorithm #clustering #data mining #mining: Evaluating a Class of Distance-Mapping Algorithms for Data Mining and Clustering (JTLW, XW, KIL, DS, BAS, KZ), pp. 307–311.
KDD-1999-ZhangRL #database #estimation #kernel #performance #scalability #using: Fast Density Estimation Using CF-Kernel for Very Large Databases (TZ, RR, ML), pp. 312–316.
KDD-1999-SyedLS99a #concept #incremental #learning: Handling Concept Drifts in Incremental Learning with Support Vector Machines (NAS, HL, KKS), pp. 317–321.
KDD-1999-Oates #clustering #identification #multi #sequence: Identifying Distinctive Subsequences in Multivariate Time Series by Clustering (TO), pp. 322–326.
KDD-1999-CortesP #agile #deployment #framework #mining #platform: Information Mining Platforms: An Infrastructure for KDD Rapid Deployment (CC, DP), pp. 327–331.
KDD-1999-Sahar #what: Interestingness via What is Not Interesting (SS), pp. 332–336.
KDD-1999-LiuHM99a #mining #multi: Mining Association Rules with Multiple Minimum Supports (BL, WH, YM), pp. 337–341.
KDD-1999-LeshZO #classification #mining #sequence: Mining Features for Sequence Classification (NL, MJZ, MO), pp. 342–346.
KDD-1999-MegalooikonomouDH #database #image #mining: Mining Lesion-Deficit Associations in a Brain Image Database (VM, CD, EH), pp. 347–351.
KDD-1999-AggarwalGY #categorisation #clustering #on the: On the Merits of Building Categorization Systems by Supervised Clustering (CCA, SCG, PSY), pp. 352–356.
KDD-1999-MannilaPS #predict #using: Prediction with Local Patterns using Cross-Entropy (HM, DP, PS), pp. 357–361.
KDD-1999-FanSZ #distributed #learning #online #scalability: The Application of AdaBoost for Distributed, Scalable and On-Line Learning (WF, SJS, JZ), pp. 362–366.
KDD-1999-KellyHA #classification #performance: The Impact of Changing Populations on Classifier Performance (MGK, DJH, NMA), pp. 367–371.
KDD-1999-BuntineFP #automation #data mining #mining #source code #synthesis #towards: Towards Automated Synthesis of Data Mining Programs (WLB, BF, TP), pp. 372–376.
KDD-1999-AdomaviciusT #personalisation #profiling #validation: User Profiling in Personalization Applications Through Rule Discovery and Validation (GA, AT), pp. 377–381.
KDD-1999-BarbaraW #approximate #data analysis #using: Using Approximations to Scale Exploratory Data Analysis in Datacubes (DB, XW), pp. 382–386.
KDD-1999-DaviesM #dataset #network: Bayesian Networks for Lossless Dataset Compression (SD, AWM), pp. 387–391.
KDD-1999-AnkerstEEK #approach #classification #interactive #visual notation: Visual Classification: An Interactive Approach to Decision Tree Construction (MA, CE, ME, HPK), pp. 392–396.
KDD-1999-DorreGS #mining: Text Mining: Finding Nuggets in Mountains of Textual Data (JD, PG, RS), pp. 398–401.
KDD-1999-ShewhartW #monitoring #topic: Monitoring a Newsfeed for Hot Topics (MS, MW), pp. 402–404.
KDD-1999-LouieK #named #visualisation: Origami: A New Data Visualization Tool (JQL, TK), pp. 405–408.
KDD-1999-RossetMNIP #challenge: Discovery of Fraud Rules for Telecommunications — Challenges and Solutions (SR, UM, EN, YI, GP), pp. 409–413.
KDD-1999-KaudererNAJ #optimisation: Optimization of Collection Efforts in Automobile Financing — a KDD Supported Environment (HK, GN, FA, HJ), pp. 414–416.
KDD-1999-HotzNPS #data mining #industrial #mining: WAPS, a Data Mining Support Environment for the Planning of Warranty and Goodwill Costs in the Automobile Industry (EH, GN, BP, HS), pp. 417–419.
KDD-1999-Chatziantoniou #data transformation #emf #sql: The PanQ Tool and EMF SQL for Complex Data Management (DC), pp. 420–424.
KDD-1999-ClearDHHLMMRSWX #information management #sql: NonStop SQL/MX Primitives for Knowledge Discovery (JC, DD, BH, MLH, PL, AM, MM, LR, AS, RMW, MX), pp. 425–429.
KDD-1999-LiuHMC #mining #using: Mining Interesting Knowledge Using DM-II (BL, WH, YM, SC), pp. 430–434.

Usama M. Fayyad, Surajit Chaudhuri, David MadiganProceedings of the Fifth International Conference on Knowledge Discovery and Data MiningKDD, 1999.

Contents (64 items)

Usama M. Fayyad, Surajit Chaudhuri, David Madigan
Proceedings of the Fifth International Conference on Knowledge Discovery and Data Mining
KDD, 1999.